Population-level impacts of pesticide-induced chronic effects on individuals depend more on ecology than toxicology

ARTICLE IN PRESS Ecotoxicology and Environmental Safety 72 (2009) 1663–1672

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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv

Population-level impacts of pesticide-induced chronic effects on individuals depend more on ecology than toxicology$ T. Dalkvist a,b,, C.J. Topping a,b, V.E. Forbes b,c a b c

Department of Wildlife Ecology & Biodiversity, National Environmental Research Institute, University of Aarhus, Grenaavej 14, DK-8410 Rønde, Denmark Centre for Integrated Population Ecology, Roskilde University Centre, PO Box 260, 4000 Roskilde, Denmark Department of Environmental, Social and Spatial Change, Roskilde University Centre, PO Box 260, 4000 Roskilde, Denmark

a r t i c l e in fo

abstract

Article history: Received 6 March 2008 Received in revised form 29 September 2008 Accepted 1 October 2008 Available online 14 May 2009

The current method for assessing long-term risk of pesticides to mammals in the EU is based on the individual rather than the population-level and lacks ecological realism. Hence there is little possibility for regulatory authorities to increase ecological realism and understanding of risks at the populationlevel. Here we demonstrate how, using ABM modelling, assessments at the population-level can be obtained even for a pesticide with complex long-term effects such as epigenetic transmission of reproductive depression. By objectively fitting nonlinear models to the simulation outputs it was possible to compare population depression and recovery rates for a range of scenarios in which toxicity and exposure factors were varied. The system was differentially sensitive to the various factors, but vole ecology and behaviour were at least as important predictors of population-level effects as toxicology. This emphasises the need for greater focus on animal ecology in risk assessments. & 2009 Elsevier Inc. All rights reserved.

Keywords: ABM IBM ALMaSS Ecotoxicology Long-term Pesticide Risk assessment Microtus agrestis Vinclozolin

1. Introduction Over the past 30–40 years increasing numbers of compounds with chronic effects have been detected in the environment, many of which come from pesticides used in agriculture. Very few of these compounds are lethal to non-target higher animals, but an increasing number have been reported to interfere with the hormone signalling system and cause sub-lethal effects in a range of species. Examples include the effects of persistent organochlorine insecticides on certain raptorial birds, the effects of tributyltin on gastropod molluscs and the effects of a mixture of pollutants on reproductive failure of fish and birds in the Great Lakes (Walker et al., 1967; Grasman et al., 1998; Gribbs et al., 1991). Responding to these cases and others, risk assessment is required before authorisation for use of chemicals or pesticides is granted. Regulatory authorities require that both acute and

$ Formal assurance is given that any studies involving humans or experimental animals were conducted in accordance with national and institutional guidelines for the protection of human subjects and animal welfare.  Corresponding author at: Department of Wildlife Ecology & Biodiversity, National Environmental Research Institute, University of Aarhus, Grenaavej 14, DK-8410 Rønde, Denmark. Fax: +45 89201514. E-mail address: [email protected] (T. Dalkvist).

0147-6513/$ - see front matter & 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.ecoenv.2008.10.002

long-term effects of pesticides be considered for obtaining effects endpoints used in risk assessments according to EU Directive 91/414. For assessing long-term effects on non-target mammals, the current risk assessment approach uses the lowest no observable effect level (NOEL) determined from a suite of endpoints measured in existing mammalian laboratory reproduction tests for comparison with an estimated theoretical exposure (ETE) level to calculate a toxicity to exposure ratio (TER). According to EU directive 91/414, its annexes and associated guidance documents, if the TER is o5, ‘‘no authorization shall be granted, unless it is clearly established through an appropriate risk assessment that under field conditions no unacceptable impact occurs after the use of the plant protection product under the proposed conditions of use’’ (Annex VI of EU Directive 91/414/EEC). Whereas human health risk assessments aim to protect individuals, according to EU guidance documents, environmental risk assessments in the European Union aim to ensure the persistence of populations and the ecosystems of which they are part (European Commission, 2000). It is widely acknowledged that population-level assessments provide a better measure of response to toxicants than assessments of individual-level effects (Etterson and Bennett, 2006; Crocker, 2005; Sibly et al., 2005). This is because, from a management perspective, the populationlevel attributes such as abundance, persistence, age composition,

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and genetic diversity are usually more relevant than are the health or persistence of individual organisms. Ignoring population- or higher-level effects and focusing only on individual-level endpoints can lead to inaccurate risk estimates and errors in environmental management decisions; often leading to an overestimation of risk, but in some cases leading to an underestimation (Pastorok et al., 2003; Bennett and Etterson, 2006; Forbes and Calow, 1999). However laboratory tests of mammals, which are the standard practice for risk assessments, measure effects of pesticides only at the individual level, and do not provide a direct measure of likely impacts at the population-level (Bennett and Etterson, 2006; Crocker, 2005; Mineau, 2005; Pastorok et al., 2003). Experimental field studies, which are used to a limited extent in risk assessments, are a point-sample of data both in time and space, and the timeframe of the study often limits the measure of effects to a few years of exposure periods and excludes recovery phase. The evaluation of risk is based on a sub-sample of the population which can be very difficult to define for mammals and birds if their ecology and behaviour is to be included, simply because of their environmental mobility. Extrapolation of the results obtained is also less than straightforward. Confounding effects, such as duration of exposure, lack of standardisation among locations, differences in the spatiotemporal factors influencing population dynamics, limited timeframe and other uncontrolled effects arising from the complexity of natural landscapes make it difficult to obtain reliable estimates of population-level effects from field studies carried out on mammals and birds. The use of population models can avoid problems associated with field tests related to lack of control of environmental factors, lack of sufficient replication and the limited timeframe available for the field study. A variety of different models exists that are available for translating impacts at the individual level to assessing risks at the population-level; these include scalar models, life-history models, metapopulation models, individual and agent-based landscape models. While scalar and life-history models, deal with the dynamics and abundance of single populations, metapopulation models deal with sets of populations that are spatially distinct but linked by dispersal. Individual-based landscape models incorporate spatially located habitat features, together with their effects on population abundance and distribution (Johnson, 2002; Cairns, 1993). The agent-based model is a further development of the individual-based model, in which the extent to which individuals react to their environment and remember past events has been implemented in greater detail (e.g. Topping et al., 2003). All these models provide a means of linking the individual to the population-level, but they differ in the amount of detail they provide. The life-history models consider the age/stage structure of populations but treat all individuals within each age/stage as identical and lack spatial structure; metapopulation models incorporate limited spatial heterogeneity in population characteristics, but also treat individuals within groups as identical; and individual and agent-based landscape models provide explicit consideration of the spatial configuration of the animals’ habitat, treat each individual in the population separately with the population response being an aggregate of the individual responses and are considered to provide the highest level of realism (Sibly et al., 2005; Topping et al., 2005). Intuitively, determining the precise section of a population most likely to be affected by a toxicant must be of over-riding importance when assessing risk. In this regard agent-based models have a number of advantages over traditional population models in being able to capture spatio-temporal dynamics and non-equilibrium properties of systems; making them capable of predicting the level of exposure the population is likely to

experience. By applying sub-lethal effects of a pesticide to different life-stages and effect levels using reliable agent-based models, more ecologically realistic estimates of the long-term consequences of pesticide use can be obtained (Crocker, 2005; Mineau, 2005; Bartell et al., 2003). Even without toxic effects of pesticides, reproductive success of individuals tends to vary with age and local habitat quality. Hence, consideration of spatiotemporal variation in the fitness of individuals is crucial for determining population-level impacts, another component that makes agent-based models capable of adding more realism to risk assessments (Pertoldi and Topping, 2004). To demonstrate the applicability of agent-based models in assessing long-term impacts of a pesticide on populations we have chosen to simulate a compound with complex long-term chronic effects. The chemical modelled is similar to the antiandrogenic compound vinclozolin (3-(3, 5-dichlorophenyl)-5-ethenyl5methyl-2, 4-oxaxolidinedione), which is a systemic dicarboximide fungicide used in agricultural crops such as oil-seed rape, fruits, vegetables, and turf grass (Veeramachaneni et al., 2006, Whitehead, 2000, Tomlin, 1997). Vinclozolin has been withdrawn from the EU market, but certain uses are still accepted in the USA (Veeramachaneni et al., 2006). An important effect of exposure to vinclozolin is that pregnant female rats exposed during a critical stage of foetus development gave rise to male offspring with heavily reduced sperm counts and deformities of the sexual organs leading to sterility or reduced fertility (Ostby et al., 1999). Recent research has indicated that these sub-lethal effects can be passed from one generation to the next for at least 4 generations (Anway et al., 2005). These effects are passed on with a high degree of fidelity down the male germ line through altered methylation states of specific gene regulatory regions making vinclozolin a transgenerational epigenetic endocrine disruptor (Chang et al., 2006; Anway et al., 2005). The adverse effects of developmental exposure to vinclozolin extend to several organs and tissues and include reduced sperm quality, sterility, prostate disease, kidney disease, immune system abnormalities, and tumour development (Anway et al., 2006, 2005; Ostby et al., 1999). When assessing the effect of a transgenerational pesticide it is important to examine the local interactions between individuals and between individuals and their environment in order to track the transmission of the inherited effect. This study utilised one of the most comprehensive agent-based risk assessment models available, the Animal, Landscape and Man Simulation System (ALMaSS) system (Topping et al., 2003). The backbone of ALMaSS is a temporally and spatially explicit simulation of landscape processes related to land use, farming decisions, and vegetation growth, making this model a very realistic assessment environment for pesticide use in agro ecosystems. ALMaSS serves the role of an experimental tool-box in which a range of pesticide usage scenarios and toxicities can be mimicked under controlled conditions. The resulting impact on animal populations can be evaluated on the basis of the best available knowledge about the species’ ecology and its interactions with its environment. This approach was used in this study to investigate the potential impact of a ‘vinclozolin-like’ endocrine disruptor with transgenerational epigenetic effects on field vole populations (Microtus agrestis) and to compare the relative importance of toxicity, exposure, and animal ecology to risk for the population. This was done by assessing the acute and long-term effects of pesticide treatment in seven scenarios in which the pesticide’s toxicity was varied by changing its DT50 (half-life) and no observable effect level (NOEL). The importance of exposure was examined by adding spatial and temporal parameters to the assessment, and by changing the treated crop type, treatment period and number of applications per year.

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2. Material and methods 2.1. Model species and model description The field vole, Microtus agrestis, is one of the most common small rodents of the countryside, and is found in grassy habitats, such as young woodland, natural grassland, and field margins (Hansson, 1971). Its diet consists mainly of the green leaves and stems of various grasses (Hansson, 1971). The habitat occupied by the vole makes it susceptible to pesticide applications by farmers. Given the abundance of the field vole, it is an ideal candidate for pesticide risk assessments. Simulations were carried out using ALMaSS (Topping et al., 2003), with addition of the ToxImpact module enabling detailed simulations of pesticide exposure and toxicology (Topping et al., 2005). ALMaSS is an agent-based model that combines two separate but interacting models; a landscape simulation and an animal population model, in this case simulating the behaviour and biology of individual field voles. Although ALMaSS is a multi-species system, only the field vole animal model was of interest in this study (see Supplementary Material for a detailed description of this model). The landscape simulation is a dynamic model composed of a GIS-based map of a real 10  10 km2 Danish landscape (Fig. 1). The map is defined using 35 landscape element types and almost 70 vegetation types. Vegetation growth models exist for all vegetation types to describe the daily changes in vegetation height and biomass (see Odderskær et al. (2006) for full description of the vegetation growth model). Weather records are used to provide driving factors for the vegetation and animal models as well as for crop management. Crop and farm management are modelled in terms of events that either affect the state of vegetation or the animals directly. Other miscellaneous landscape events such as cutting of roadside vegetation and traffic loads on roads are also simulated. Landscape heterogeneity is therefore controlled spatially by the topography and by cropping choices of the farmer and temporally by weather, vegetation development, and management. For all simulations carried out, the crop rotation used on all farms resulted in the following crop distribution by agricultural area: winter wheat 20%, spring barley 20%, set-aside 10%, field peas 10%, winter rye 10%, winter barley 10%, clover grass 10%, and winter rape 10%. Furthermore, 10% of the agricultural area was defined as stationary orchards, and 1.75% of the landscape was covered with natural grassland. The spatially heterogeneous environment in the model provides locally based information used by the animals to make decisions related to their ecology and behaviour. The spatial resolution of the model is 1 m2, and the time-step is 1 day. This means that all management carried out on fields, vegetation growth, and weather have a temporal resolution of 1 day. The field vole model describes the behaviour and ecology of M. agrestis based on field studies and scientific literature. Where possible the parameters and behaviours incorporated have been taken from non-cyclic Scandinavian populations to closely simulate Danish vole populations. Nine main behavioural states exist (evaluate and explore (specific ones exists for the males, females, juveniles ,and dispersing individuals), infanticide, mating, gestation, give birth, lactation, maturation, dying, and special explore (which the females perform after weaning and infanticide). The model vole obtains detailed information from its local

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surroundings in the model environment (e.g. habitat type, vegetation growth and farm practice), and conspecifics (e.g. sex, susceptibility to mating and territory) which allows each individual to react to both stationary and dynamic factors arising from its local environment. Dependent on probabilities of internal (e.g. age, fitness, pregnancy) and external (e.g. farm management, conspecifics) events, the vole may shift from one state to another (e.g. dying or give birth) and can engage in a number of behaviours during the model’s time-step of 1 day. Vole movement is defined by three parameters describing the direction, strength of the direction, and number of steps. Dependent on a vole’s behavioural state different parameter combinations of movement are applied to allow for a gradient between exploratory and directional movements. The resulting distribution of voles is an emergent property largely based on habitat preference and dispersal ability. The mature vole starts each day by evaluating the surrounding habitat based on the vegetation quality (available food and cover), and for the males during the breeding season, the number of females. A territory is established if the habitat quality is above a set level and the extent of overlap with older voles of the same sex is acceptable; otherwise the behavioural state will be altered to dispersal. For voles with a territory, the area is evaluated daily to allow the possibility of optimising conditions by local movements and to register and act on local environmental changes. The emergent distribution of voles is thus dependent on the sum of many local interactions and is influenced by factors such as vole abundance, farm management, habitat preference, as is the case in real life.

2.2. Toxicological implementation The toxicology is only loosely based on vinclozolin since the present assessment’s aim is to demonstrate the utility of the approach and critical aspects of the current evaluation rather than to make a formal risk assessment for this particular pesticide. Vinclozolin was chosen as the starting point for this example because of its well-documented and unusual toxicology, and because the complexity of the resulting vole/pesticide system provided a significant challenge. The ABM approach and ALMaSS provide great flexibility in the incorporation of toxicology, environmental fate, and exposure routes and can thus copy mechanisms if data is available. In this case the following implementation of toxicology, exposure, and fate was chosen to represent a sensible general case.

2.2.1. Toxicology In the simulations, epigenetic effects were modelled as 50% sterility of male offspring of the dams exposed above NOEL. The reduction in sperm quality and adult onset of disease were modelled as 50% reduction in mating success of the non-sterile male offspring of the exposed gestating mother. The epigenetic transgenerational transmission of the effect would cause the reduced fitness to be inherited through the male germ line causing 50% of the male offspring to be sterile and 50% to have reduced mating success. It is not known to what extent sterile males attempt to mate. The worst-case scenario was assumed for this study, in which females showed no discrimination against affected males, i.e. affected voles were capable of behaving and producing

Fig. 1. 10  10 km2 default landscape used for all the simulations. Additional natural grassland was added to the original map to cover a total of 1.75% of the landscape and 10% stationary orchards was added to the agricultural part.

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pheromones in the same way as non-affected voles. However, if a female mated with a sterile male she did not experience a ‘‘false pregnancy’’ and would mate again the following day. Whether this second mating would be with the same sterile vole would depend on whether her territory overlapped with another male; if so the probability of mating would depend on the degree of overlap and thereby local density. In the vast majority of cases toxicological data on chronic effects is sparse and dose–response curves are not required for regulatory assessments, a threshold response characterised by the NOEL being the standard descriptor. Hence the simulations carried out here use a threshold response, although there is nothing to prevent a dose–response curve being implemented in this type of approach. The female voles were modelled as transmitting the epigenetic alteration on to her male offspring if they ingested a dose of the pesticide above the NOEL in any 1 day within the critical gestation period of days 16–21. No bioaccumulation was assumed.

2.2.2. Exposure and environmental fate The application rate was assumed to be 750 g/ha and applied directly to surface vegetation, i.e. not to the trees themselves in the orchards, alternatively this could be viewed as 1500 g applied to the trees with 50% retention by the canopy. The amount of pesticide reaching the surface vegetation was assumed to be evenly distributed through the available biomass, hence higher biomass would result in lower dosage for the same weight of plant material ingested. The version of ALMaSS used utilises the ‘ToxImpact’ module to enable modelling of pesticide deposition to crop and off-crop areas at a spatial resolution of 1 m2. The residue concentration of pesticide on the fields was recalculated every 24 h based on the pesticide’s half-life. When the crop was sprayed twice a year, the pesticide concentration was the sum of the new application residue and that remaining from the previous application. Once the concentration of the pesticide was below 0.01 g/ha it was assumed to be zero to avoid infinitely small calculations. Drift of the pesticide to off-crop areas was also considered in the model by the equation: y ¼ 2:75ðx þ 1:87Þ  2:12

(1)

where y is the concentration of pesticide at x distance from the cell of application. This equation is based on drift data from the spray drift calculator within FOCUS’s surface water scenarios SWASH software (FOCUS, 2001). Each 1 m2 cell to which the pesticide was applied received a dose of 750 g/ha. This amount was distributed to all cells in a 12 m radius according to Eq. (1). In this way the cells close to the treated cell would receive a higher proportion of the pesticide. The cells surrounded by other cells with pesticide treatment in a radius of 12 m received the total 750 g/ha pesticide, whereas cells closer than 12 m from the edge of the field received a lower amount, because some of the pesticide applied would drift to off-crop areas. This method automatically accounted for irregularly shaped field boundaries without the need for complex calculations. To reduce the number of calculations necessary, distance (x) was measured with a resolution of 4 m. The mean concentration (y) within each 4 m section was calculated according to the equation and deposited in those cells. In this way was the three concentrations deposited within a radius of 12 m from each treated cell. The dosage ingested was calculated based on the vole position at the end of each simulation day. No avoidance was assumed and if a vole was positioned in a contaminated area the vole was assumed to feed only in this area and ingested a dose calculated as Pesticide intake ðmgÞ ¼ typical weight dam ðkgÞ  ingestion rate ðkg food=kg bwÞ  residue concentration ðmg=kgÞ

(2)

The typical weight of the dam (25 g) and ingestion rate (1.39 kg food/kg body weight (bw)) was used for the pesticide intake calculation (Crocker et al., 2002). The residue concentration was calculated as mg pesticide per kg plant biomass.

2.3. Model scenarios All population density measurements were based on 35 replicate simulations for each scenario. Thirty-five was chosen as the point at which adding further replicates did not change the relative impacts of the different scenarios, and since the model requires a number of hours per replicate it was desirable to avoid unnecessary simulation runs. Data were collected from each replicate simulation given the number of voles present on 31 December in each simulation year in the whole landscape. A baseline (control) simulation was created with the same default settings for the landscape, but with no pesticide application. The application rate of the pesticide was applied to the orchards once a year on 31 May. No application occurred for the first 30 years, followed by a 30-year period of pesticide application and then a subsequent recovery period with no spraying for 60 years, resulting in a 120-year simulation run. The half-life was set at 7 days and voles were assumed to exhibit toxic effects if they ingested an exposure above the default NOEL of 25 mg/kg bw in any 1 day during days 16–21 of gestation.

Table 1 Typical vole densities on 1 June in ALMaSS habitats prior to the period of pesticide application Habitat type

Area (ha)

Voles/ha

Hedgesa Roadside vergea Field boundarya Wetland/bogs/wet meadow Field Permanent pasturea Unmanaged grasslanda Deciduous forest Mixed forest 413 Coniferous forest Young foresta Orcharda Other

28 88 18 112 4980 789 175 614 413 860 146 501 1276

27.5 35.7 74.2 7.6 1.7 22.2 64.0 0.3 0.6 0.1 44.3 18.7 0.9

a Habitats assumed to have grassy surface vegetation to a significant degree. In the case of orchards the grass is assumed to be mown once per year.

The orchards were not part of the farmers’ crop rotation, but were stationary and distributed across the agricultural part of the landscape at random. The orchard size was in the range of 10,000–70,000 m2. It is standard practice within orchards for mowing operations to be carried out throughout the growing season, typically varying between one to four cuts. For this study the number of cuts was set to one just before harvest. The pesticide applied to the orchards was assumed to fall through the canopy and be applied to the vegetation between the trees. This was modelled in order to make comparisons across different crop types feasible and to avoid the impact of the pesticide to be based on the amount applied but rather to focus on the effect once it was present. For the weather data a mean for the period from 1989 to 1999 of daily temperature, rainfall, and wind-run records was used in order to minimise the effect of annual weather variation and get a clearer view of the impact of the pesticide. At the start of the simulation, 55,000 individual voles were randomly scattered in the landscape, creating an initial high and ubiquitous vole presence across the landscape. Voles survived, died in or dispersed from their start locations depending upon their local condition. The result was high population variability at the start of the simulation, and therefore data from the first 20 years of the simulation run were excluded from the analyses. Table 1 shows the density of voles in different habitat types as a mean across the landscape prior to pesticide applications. The scenarios used the set of default parameters unless otherwise stated, and only one parameter was varied within each scenario. The scenarios are (Table 2): Worst-case scenario: All the model voles in the population were assumed to ingest contaminated vegetation and consume above NOEL regardless of their position in the landscape or the timing of pesticide application. For all other scenarios the voles only triggered toxic exposure if (i) the dam was in 16–21 days of her pregnancy, (ii) she consumed pesticide-contaminated food, and (iii) she consumed a dose above 25 mg/kg bw within 1 day. NOEL scenario: A series of six levels of NOEL were considered by halving the value of 50 mg/kg bw/day five times down to the value of 1.5625 mg/kg. DT50 scenario: The pesticide half-life was modelled with a range of values: 3, 7, 14, 28, 56, or 112 days. Treated crop scenario: The pesticide was applied to clover/grass leys (clover grass scenario), winter rape (winter rape scenario) or orchards (default scenario). Treatment period scenario: Eight simulations were carried out in which the period of pesticide application was varied as 60, 50, 40, 30, 20, 10, 5, or 1 year. Treatment intensity scenario: In contrast to the other scenarios, two applications per year were implemented. Two different spraying regimes were modelled with the same field rate per application as for one treatment (750 g/ha). Interval scenario: Four scenario runs were created where the period between the two applications was changed by doubling the interval from 7 up to 56 days, with the first application date on 31 May for all of the simulations. Starting date scenario: The period between the two applications was 1 week, and the starting date for the first pesticide treatment application was modified. Ten simulations were created with the first application date on 19 April, 26 April, 3 May, 10 May, 17 May, 24 May, 31 May, 7 June, 14 June, or 10 September.

3. Results For species with high year-to-year variation difficulty of extrapolating the effect of the pesticide from other factors within the landscape can be substantial and requires a larger number of

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Table 2 Overview of the scenarios considered in this study Scenarios

Worst case NOEL DT50 Treated crop Treatment period Treatment intensity

Toxicity

Exposure

NOEL

DT50

Crops treated

Treatment length

Applications per year

All* All** (6) D D D D

D D All** (6) D D D

All* D D All** (3) D D

D D D D All** (8) D

D D D D D ALL** (14)

D, default setting. All*, all male voles expressed the effect of the pesticide regardless of their position in the landscape or whether the dam had been exposed. All**(), the full range of settings described in the text for this parameter were used. Number of settings is listed in parenthesis.

NRTB ¼ ða=ðyeard ÞÞ=ð1 þ expððb  yearÞ=cÞÞ

(3)

This function (Eq. (3)) is based upon the logistic equation (Eq. (4)), traditionally used in population ecology to describe population growth, but with the addition of a power curve NRTB ¼ a=ð1 þ expððb  yearÞ=cÞÞ

62000

Population Size

60000 58000 56000 54000 52000 50000 20

40

60 80 Simulation Year

100

120

Fig. 2. Default scenario: The grey lines show five randomly picked replicates and the black line is the mean of the 35 replicates with 95% confidence interval bars.

Pesticide phase

Recovery phase

1

0.96 NRTB

replicates. In order to overcome potentially high background variation in the landscape (Fig. 2) the impact was measured as population size relative to baseline (NRTB) for the vole population in the whole landscape. The baseline was identical in all respects to the simulation with pesticide treatment except for the lack of pesticide application. Using NRTB provided a simple way to exclude variation in other factors, e.g. crop rotation, which would otherwise obscure the pesticide impact. Fig. 3 illustrates the NRTB results from the NOEL scenario. The simulation results have been divided into three phases. The figure shows a stabilisation phase with no pesticide treatment for the years 20–30. At year 31 the pesticide application phase started where the pesticide was applied once a year for the following 30 years on 31 May, which resulted in a population perturbation and a change in population size (pesticide phase). Year 60 was the last year with pesticide application, after which the population entered a recovery phase for the remaining 60 years of the simulation (recovery phase). The population experienced an increase in population size towards a new carrying capacity. The stabilisation phase in the figure is representative for all the scenarios, and represents the background inter-run variability. In order to describe the population perturbation and level of recovery objectively a curve was fitted to the pesticide and recovery phase of the NRTB curve for each scenario simulation. Variations of logistic, polynomial, exponential, and power functions, were tested by fitting to the NRTB curve using the nonlinear regression library (nls) in R (Bates and Chambers, 1992; Bates and Watts, 1988). The Akaike Information Criterion (AIC) (Burnham and Anderson, 2002) was used to compare the fit of each function to the all data. The median AIC value for each function was used as a measure of the most parsimonious fit across all simulations’ pesticide and recovery periods. The lowest median AIC value was given by

0.92 50 25 12.5

0.88

6.25 3.125 1.5625

0.84 20

30

40

50

60

70

80

90

100

110

120

Simulation Year Fig. 3. NOEL scenario: NOEL was halved five times from 50 to 1.5625 mg/kg bw. The 30-year period with pesticide application is illustrated by the pesticide phase and the recovery period refers to the last 60 years of the simulation after ended pesticide treatment.

(4)

The addition of the power curve adds a ‘softer’ asymptote and this model describes the data very well and indicates that, contrary to expectations, the new ‘carrying capacities’ reached after year-on-year pesticide application (pesticide phase), or recovery (recovery phase) are not stable, but continue to change throughout the simulation (see Fig. 4 for an example of the NOEL 12.5 recovery phase). Curves were fitted separately for both pesticide and recovery phases. The relative rate of change in NRTB (the derivative of Eq. (3)) was calculated at the start and the end of both the

pesticide and the recovery phases and has been denoted rpest(31) rpest(60), rrec(61), and rrec(120), respectively. In order to avoid extrapolating the function beyond the fitted interval the derivative at year 120 was used to measure the time to recovery (NRTBX1). These values give an objective method for comparing the effects of the factors varied in each scenario (Table 3). General observations: The relative rate of change decreased within each phase as the simulation time elapsed. In all cases the population did not reach stability either during the pesticide treatment or in the subsequent recovery phase (rpest and rreca0)

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0.98

NRTB

0.96

0.94

0.92

0.90

0

10

20

30 Year

40

50

60

Fig. 4. The fit of the three functions with the lowest median AIC value. The round dots represent the population size relative to baseline for the recovery phase of the NOEL 12.5 mg/kg bw simulation.

and full recovery was not obtained in any of the scenarios during the 60-year period (Table 3). As the toxicity level of the pesticide or the length of exposure was increased the initial rate of decrease in NRTB increased (rpest(31)), and the same general trend was observed at the start of the recovery phase resulting in an initially faster increasing population size after pesticide cessation (rrec(61)). The recovery values of NRTB(120) were correlated with the values of NRTB(60), but rrec(120) was not constant and appeared to decrease at low NRTB(60) levels. This suggests that a threshold value of NRTB(60) exists below which recovery is more difficult to obtain. The default scenario: For the default orchard scenario carrying capacity was reduced from 1 to 0.92 NRTB(60) during the years with pesticide application (pesticide application phase, Table 3). After pesticide cessation, the population size increased to 0.99 NRTB(120). The initial rate of change was 1.61% and 1.67% (NRTB/year) for the pesticide and recovery phase, respectively and rpest(60) and rrec(120) was 0.029% and 0.033% (NRTB/year). Recovery was reached after 210 years. The worst-case scenario: In contrast to all other scenarios, the voles did not experience an increase in population size after the pesticide treatment was stopped (Table 3). Analyses of the NRTB showed a continual decrease in population size from 1 to 0.19 for the simulated period, and recovery did not occur (Table 3). NOEL scenario: The initial rate of change increased as NOEL was repeatedly halved from 50 to 1.5625 mg/kg bw producing a decreasing level of NRTB(60) from 0.94 to 0.85 (Table 3 and Fig. 3). The reduction in NRTB(60) resulted in a population depression from 6% to 15% (factor 2.5) compared to a lowering of the NOEL by a factor of 32, indicating a relatively low sensitivity of the population dynamics to the NOEL. A threshold value of 0.96 NRTB(120) seemed to exist for this scenario at and below which a separation in the relative recovery level occurred. This resulted in a doubling of recovery time for NOEL 3.125 and 1.5625 mg/kg bw compared to the other simulations in this scenario (Table 3). DT50 scenario: By increasing DT50 from 3 to 112 days a relative worsening of rpest(31) from 1.28 to 5.86 (NRTB/year) occurred producing a decrease in NRTB(60) for the pesticide phase from 0.95 to 0.78 (Table 3). Compared to the NOEL scenario (factor 2.5 decrease in NRTB(60)), the DT50 scenario resulted in a factor 4.4

population depression when the pesticide’s half-life was doubled five times from 3 to 112 days. This indicates that the voles were much more sensitive to changes in DT50 than NOEL. However increasing the DT50 above 28 days, only produced minimal further impact on NRTB(60) (NRTB changed from 0.80 to 0.78), compared to a change in the NRTB from 0.80 to 0.95 when DT50 was reduced from 28 to 3 days (Table 3). In the recovery phase the same pattern was observed. rrec(61) increased as DT50 was increased, but above 14 days the effect was minimal. As in the NOEL scenario the recovery times seem to be grouped suggesting threshold levels of NRTB(60) below which recovery was hard to obtain. Treated crop scenario: No effect was detected for the clover grass and winter rape scenarios when the pesticide was applied to these crops (Table 3), although female voles were exposed to the pesticide in both of these scenarios. See default scenario above for orchard results. Treatment period scenario: Fig. 5 shows that all the NRTB curves for this scenario follow the same trajectory. The standard curve could not be fitted to these data because too few points were available for the short periods of decrease in the pesticide phase and increase in the recovery phase to allow a fit. However the pattern is clear. The level of change in NRTB was initially steep and continued to decline (following the curve for the default scenario treatment phase, Fig. 2). The rate of increase seemed to be affected by the level of NRTB such that a high depression in the pesticide phase resulted in a lower recovery population size by year 120. Treatment intensity: The interval scenario showed a trend towards a relatively higher rpest(31) and a lower population size as the interval between the applications was increased (Table 3). However the level of impact was not in direct proportion to the length of time between the applications. Population perturbation increased with increasing interval between treatments, but with negligible extra impact above 28 days. A difference in the recovery level was detected in accordance with the decrease in NRTB(60). As with the other scenarios the recovery population size was related to the size of the population perturbation. The starting date scenario indicated that the assessment was sensitive to the timing of the pesticide application with a range of reductions in population size from 0.96 to 0.87 NRTB(60). Applications in April caused the greatest depression as well as the longest time to relative recovery, and the least effect was observed with application in September, the latest application simulated.

4. Discussion The ALMaSS vole model used here is designed to realistically represent the behaviour and population dynamics of M. agrestis in the Danish landscape. However, for the purposes of this study we have used this model, as a manipulative experimental system to demonstrate the type and scale of effects brought by increasing the realism of population-level risk assessments. Naturally increasing realism is on a continuous scale and further improvements can always be made. For instance the simulation could have been made more realistic by including the real daily weather parameters for the 10-year period instead of using the mean. By using mean weather we excluded the extremes and hence potential interactions between the weather and pesticide effects was unaccounted for in our simulations. In this study, mean values were used to remove weather as a factor for purposes of clarity of the results, but realistic weather should be included in any real risk assessment since this interaction effect could certainly be significant. Another refinement which could be added in the case of a real risk assessment would be to describe uncertainty in the results by means of a sensitivity analysis of the model parameters with

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Table 3 Initial rate of change (rpest, rec(31, 61)), population perturbation level (NRTB(60, 120)) and final rate of change (rpest, together with the estimated recovery time for the simulated scenarios (see Fig. 5 for treatment period scenario) Scenario

Pesticide phase

rec(60,

1669

120)) for the phases of pesticide and recovery

Recovery phase

rpest*(31)%

NRTB**(60)

rpest*(60)%

rrec*(61)%

NRTB**(120)

rrec*(120)%

Recovery time***

Worst case Worst case Orchards (default)

9.04 1.61

0.27 0.92

0.197 0.029

– 1.66

0.19 0.99

0.186 0.033

– 210

NOEL 50 25 (default) 12.5 6.25 3.125 1.5625

1.50 1.61 2.11 2.31 3.11 3.36

0.94 0.92 0.90 0.88 0.86 0.85

0.019 0.029 0.021 0.013 0.011 0.011

1.88 1.66 2.23 2.43 2.55 2.44

0.99 0.99 0.98 0.98 0.96 0.95

0.017 0.033 0.052 0.047 0.031 0.029

238 210 192 228 396 466

DT50 3 7 (default) 14 28 56 112

1.28 1.61 3.33 4.81 4.93 5.86

0.95 0.92 0.85 0.80 0.79 0.78

0.043 0.029 0.002 0.012 0.016 0.021

1.38 1.66 2.64 2.78 2.94 2.99

0.99 0.99 0.95 0.95 0.94 0.94

0.020 0.033 0.035 0.037 0.034 0.035

189 210 391 400 496 503

Treated crop Clover grass Winter rape Orchards (default)

– – 1.61

– – 0.92

– – 0.029

– – 1.66

– – 0.99

0.033

– – 210

Treatment intensity Interval 7 14 28 56

1.95 2.77 3.94 4.21

0.89 0.87 0.85 0.85

0.003 0.032 0.029 0.038

2.14 2.46 2.48 2.31

0.97 0.96 0.95 0.94

0.040 0.039 0.033 0.022

254 312 451 636

Starting date 19A, 26A 26A, 3M 3M, 10M 10M, 17M 17M, 24M 24M, 31M 31M, 7J 7J, 14J 14J, 21J 10S, 17S

2.76 3.53 2.43 2.30 2.54 2.31 1.95 1.77 2.18 0.13

0.88 0.87 0.90 0.91 0.91 0.90 0.89 0.89 0.89 0.97

0.013 0.035 0.051 0.066 0.064 0.046 0.003 0.018 0.034 0.011

2.21 2.53 2.22 1.48 1.99 2.44 2.14 1.89 1.79 0.92

0.97 0.97 0.98 0.98 0.98 0.98 0.97 0.98 0.98 0.99

0.038 0.043 0.047 0.032 0.039 0.048 0.040 0.038 0.045 0.014

275 281 202 238 207 201 254 247 215 212

– –

1

NRTB

0.98 60 50 40 30 20 10 5 1

0.96 0.94 0.92 0.9 30

50

70 90 Simulation Year

110

Fig. 5. Treatment period scenario: the number of years with pesticide treatment was varied from 1 up to 60 years.

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regard to both the vole and pesticide assumptions. Unfortunately a full sensitivity analysis is an extensive process requiring many thousands of simulation runs each of approximately 12 h duration on standard PC architecture, and therefore was not within the scope of this paper; it is however the subject of ongoing study (Dalkvist et al., unpublished). Other areas worth further study could revolve around the implementation of toxicological information. Since the model is highly individually specific in space and time, and thus exposure varies greatly between model voles, the impact of incorporating a dose–response curve rather than a threshold effect could be investigated. In fact the list of potential improvements and interesting experiments with model structure is almost endless, which is both a strength and a weakness of the ABM approach. In this study we have chosen a level of realism far greater than that normally applied to risk assessments, and discuss the results in the light of this level of model complexity. The worst-case scenario is the most extreme example which can be seen to be equivalent to the screening assessment or tier one assessment required in EU regulatory testing (EU Directive 91/414/EEC). In this scenario the tier one assumptions were analysed at the population-level, where all voles within the simulation were exposed regardless of their position within the landscape and the timing of the pesticide application. Comparison to the much more realistic default scenario shows not only a large quantitative change in the predicted effect, but also a qualitative difference in that recovery is not predicted in the worst-case scenario (Table 3). The qualitative change is due to the lack of spatio-temporal dynamics for the first tier assessment. This means in this case, that all voles would be affected, no purging of the methylation alternation would be possible, and all the male voles would therefore be expressing the methylation alteration. This indicates the importance of considering toxicological aspects together with spatial dynamics. Our simulations demonstrate the potential impact of a number of important factors on the results of the assessments related to the toxicity of the pesticide (NOEL and DT50) and the extent of exposure (treated crop, treatment period, treatment intensity; interval and starting date) 4.1. Toxicity (i) NOEL: We examined the sensitivity towards NOEL by halving the trigger value five times, from 50 to 1.5625 mg/kg. The results showed a factor 2.5 increase in the population depression (Table 3). By reducing the NOEL level the timespan within which the female voles would be at risk of consuming a sub-lethal dose of the pesticide would increase, therefore the impact ought to increase as the exposure period increases. However since we do not have rapid mixing of the population, those voles exposed are largely the same regardless of the NOEL. Consequently there are maximum proportion of voles exposed that is related to the area treated, plus a small number that disperse into these areas during the time when the compound is present. This reduces the impact of a lower NOEL. (ii) DT50: The DT50 receives little attention in current pesticide risk assessments, however, altering the DT50 from 3 up to 112 days had a very large impact on the population depression. The scale of the effect was clearly correlated with the breeding season where the half-lives 28, 56, and 112 days went close to or beyond the end of the breeding season and resulted in the same range of population perturbation and recovery level. The results showed a higher sensitivity towards the value for the pesticide’s DT50 than to the changes in the NOEL, due to the first-order kinetics of decay for the pesticide: C ¼ C 0 ekt 3k ¼ ðlnðC=C 0 ÞÞ=3k ¼ ln 2=DT50

(5)

where C is the concentration of the residue at time t, C0 is the residue concentration at the start and k is a rate constant for loss which is dependent on DT50. Increasing DT50 reduces k and thereby the term kt which increases the period of exposure exponentially; whereas changing NOEL is equivalent to changing the constant C in Eq. (5). This would result in a small change of the time period of exposure (t) compared to changes in k hence the greater sensitivity to DT50.

4.2. Exposure When considering exposure, reliable estimates for humans are usually possible based on levels in food and water, dosage levels of drugs or levels of contaminants in the air (Walker, 2006) whereas, levels of exposure are notoriously difficult to estimate in terrestrial habitats such as farmland. The chemical concentrations in or on potential food items vary over time and space, and consequently an animal’s daily chemical intake will not be constant (Fischer, 2005). The likely pesticide level consumed within the treated fields in the landscape at the time when residues exceed the NOEL is very difficult to predict with any degree of accuracy for mobile animals although the spatial and temporal factors in ALMaSS make it possible to model a more realistic exposure than when the exposure is based on estimates and assumptions of vole distribution and behaviour alone. The degree of exposure was evaluated in four scenarios: treated crop, treatment period, treatment intensity; starting date, and application interval. For the treated crop scenario the habitat preference of the voles was critical to the level of impact observed. Hansson (1977) documented the field voles’ habitat to consist of 80–90% continuous ground cover containing either green or decaying vegetation to allow for a continuous diet and shelter for the voles. These preferences are built into ALMaSS and cause the voles to avoid dense forests and most cultivated fields, including winter rape. In the winter rape scenario only voles in field margins and dispersing voles would be at risk of ingesting a dose of the residue, which is a very small proportion of the total vole population. The fields with clover grass might at first sight seem to fulfil the voles’ preferences for habitat, however, in European agricultural landscapes, and in ALMaSS, intensive farming practices cause the grass to be cut regularly for silage or be used for grazing livestock. Evans et al. (2006), Schmidt et al. (2005), and Jensen and Hansen (2001), found that intensive livestock grazing would negatively affect the abundance of field voles. The effect of a silage cut would be to remove vegetation cover from the voles’ habitat and potentially directly or indirectly kill a large proportion of the voles present in the grass fields throughout the growing season. Both cutting and grazing will cause surviving voles to disperse from the fields and thereby increase the mortality rate of these. In contrast to the clover grass and rape fields, the orchards provide the voles with ground cover and food in the form of grass and other weeds growing between the sparsely planted trees. The management of the orchards simulated in ALMaSS allowed the farmer to cut the ground vegetation once before harvest, which allowed the voles to reside and breed within these areas until late season, and hence resulted in a relatively large potential for exposure. Hence, even though the area treated and treatment rates were identical between the three scenarios, the incorporation of realistic behaviour resulted in differentiated exposure and subsequent population impacts. The length of the pesticide application in the treatment period scenario was directly related to the level of population perturbation because rpest(31) for all treatments was identical, but the recovery population size at 90 years of the simulation was correlated with the population size at the end of the pesticide

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phase (Fig. 5). This was because the rate of population decrease slowed during the pesticide application period. This suggests that as pesticide application continues we gradually reach an equilibrium state between pesticide-affected and non-affected voles rather than the continually decline in population size shown in the worst-case scenario. A more complex picture was obtained by altering exposure by applying the pesticide twice to the orchards, first by increasing the interval between the applications and then by changing the starting date (Table 3). The additional treatment increased the impact compared to the default scenario by reducing the NRTB(60) from 0.91 to as low as 0.83, approximately doubling the impact. However, this effect was greatest with a longer inter-application period and for applications that occurred early in the breeding season (from when the grass starts to grow to October; Myllima¨ki, 1977). This indicates the importance of considering the life cycle of the organism under study together with the temporal pattern of pesticide application. This impact will be even greater in organisms with synchronised breeding, i.e. many species of mammals, birds, and arthropods, since pesticide treatments may be timed to coincide or miss critical periods in the breeding cycle.

4.3. General observations All simulations indicated that the size of the population after 60 years of recovery was lower than the non-treated baseline scenario (Table 3, Fig. 5). This result could have been related to the epigenetic effect of the pesticide, but investigation of the amount of affected voles within the model showed that the gene was purged from the population after only a short period. In fact the phenomenon was related to the spatial dynamics of the voles in this fragmented landscape. Even small perturbations of the population can mean local extinction for small sub-populations, and depending on their location relative to larger source populations, the time to recolonisation will vary. If the perturbation is large then this effect is exacerbated, and the unusual form of the recovery curve is created by initially a rapid population growth in core habitats that manifests as high rrec(61) values, and as the range of the vole expands, habitats outside the core will slowly be recolonised and lower rrec(120) values is produced. The same mechanism, together with epigenetic breeding depression, explains the continual decline of the voles, as patches slowly become empty and the vole contracts to core habitats. By ignoring the spatial scale, related animal ecology and behaviour, as in the current risk assessment, the spatial mechanisms and possible local extinctions are unaccounted for. Thus the inclusion of the spatial mechanisms provides a new dimension to the risk assessment and suggests that landscape structure might be an important variable. A summary of the measured effects shows that the worst-case scenario caused the highest population-level impact of all the simulated scenarios (Table 3). In this scenario all voles were affected and no purging was possible resulting in a continually declining population through the measured period after cessation of pesticide application. The addition of a more realistic pesticide application and toxicology resulted in a lower impact for the rest of the scenarios and a more realistic assessment of risk (Table 3, Fig. 5). The DT50 and interval scenarios caused the highest impact of the more realistic scenarios, and the scale of effect was greater than the NOEL scenario. In the current risk assessment most consideration is given to the evaluation of an accurate measure of NOEL for the laboratory test organisms, and little attention is given to documentation of a pesticide’s rate of decay and ecologically relevant application periods, which our observations demonstrate to be highly relevant for the risk assessment. Starting

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date scenario had an impact just below that measured for the NOEL scenario. The treatment period had an impact between 0.99 and 0.91 NRTB(60) (Fig. 5). The repeated exposure year after year for up to 60 years had lesser impact on the population size than the exposure analysed in the other scenarios. This indicates that the voles were more sensitive to extension of the exposure period within the breeding season than prolongation of the years with pesticide application, where the voles have more time to recover between the treatment periods. For a pesticide such as vinclozolin that affects males in the uterus it is highly relevant to consider the extent of exposure throughout the breeding season in order to make a realistic evaluation of risk. In fact, considering the timing of the pesticide effects in relation to the ecologically realistic susceptible period of the organisms under study is probably essential for the majority of pesticides risk assessments. The treated crop scenario had an impact on the NRTB(60) around the same level as the treatment period scenario, however the scale of effect was between 0% and 9% (Table 3), which again illustrates the importance of considering the ecology and behaviour of the animal considered at risk when deciding pesticide application restrictions. Overall these results demonstrate that to obtain a realistic estimate of risk it is crucial to take ecological, spatio-temporal and toxicological factors into account in a population-level risk assessment. Whilst we cannot be sure that all important factors are taken into account in a simulation, ABM simulation can be carried out by incorporating all known factors into the model, and thus provide assessments based upon the principle of best available knowledge. In practice where significant concerns are raised by the lower tier assessments, the evaluation is taken to a higher tier which aims to provide increasing realism by including further details of known ecological processes operating under field conditions and under the proposed conditions of use (Annex VI of EU Directive 91/414/EEC). However regulatory authorities experience difficulties when assessing long-term pesticide risk because of the lack of accepted methods for (i) refining characterisations of toxicity and exposure, (ii) extrapolating chronic toxicity data to untested species, (iii) resolving mismatches between laboratory and field exposure profiles, and (iv) evaluating individual-level effects at the population-level (Walker, 2006; Bennett et al., 2005; Mineau, 2005). The modelling procedure used in this study can help to alleviate the problems by objectively analysing the impacts of different assumptions regarding toxicology and exposure. Furthermore, individual effects can be translated to population effects using the same mechanisms that occur in the real world. The evaluation procedure presented here is relatively simple, objective, and provides highly descriptive results that can be used to assess the potential risk under a range of assumptions.

5. Conclusion Using ALMaSS together with a complex test case scenario of a pesticide causing transgenerational chronic reproductive depression as an experimental system demonstrates the type and scale of effects that increasing realism can bring to a population-level risk assessment. Temporal and spatial factors together with animal ecology and behaviour have large impacts on the level of exposure and are therefore relevant to risk assessment. Our results show that accuracy of the assessment of risk can potentially be increased if more consideration is directed at determining the level of exposure. The type of crop, time of application, number of applications, and the interval between treatments all have a large impact on the level of reduction in population size, and these responses are tightly interlinked with

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the ecology and behaviour of the species considered at risk. In the case of pesticides causing transgenerational impacts, the level of interactions and thereby spatial and temporal factors that link exposure to the breeding season and areas of the voles’ habitat are particularly important to consider when assembling a realistic assessment of risk. Although the link between exposure and risk is obvious, there is currently little attention concentrated on accurate estimates of exposure, rather the focus is on reliable toxicological estimates from laboratory tests, and particularly the NOEL. Our results call for a shift in focus in risk assessments away from purely toxicological tests and towards greater consideration of spatial and temporal components together with animal ecology and behaviour. The ABM approach here proved to be a powerful tool for elucidating various factors related to population-level risk assessment. Even though ALMaSS is not as complicated as the real world, using the level of realism chosen for this study, the model was able to simulate many real world complexities and interactions and demonstrates the importance of these when carrying out an ecotoxicological risk assessment. We would therefore recommend the wider use of ABM simulation in carrying out complex risk assessments in the future.

Acknowledgments This research has been sponsored by the Danish Natural Science Research Council. Thanks to Jacob Nabe-Nielsen, Richard Sibly, and Thomas Kragh for statistical and mathematical assistance, and to two anonymous reviewers for their constructive comments.

Appendix A. Supplementary Materials Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ecoenv.2008.10.002.

References Anway, M.D., Cupp, A.S., Utzumcu, M., Skinner, M.K., 2005. Epigenetic transgenerational actions of endocrine disruptors and male fertility. Science 308, 1466–1469. Anway, M.D., Leathers, C., Skinner, M.K., 2006. Endocrine disruptor vinclozolin induced epigenetic transgenerational adult-onset disease. Endocrinology 147, 5515–5523. Bartell, S.M., Pastorok, R.A., Akc- akaya, H.R., Regan, H., Ferson, S., Mackay, C., 2003. Realism and relevance of ecological models used in chemical risk assessment. Hum. Ecol. Risk Assess. 9, 907–938. Bates, D.M., Chambers, J.M., 1992. In: Chambers, J.M., Hastie, T.J. (Eds.), Statistical Models in S. Wadsworth and Brooks/Cole. Pacific Grove, CA, pp. 377–421. Bates, D.M., Watts, D.G., 1988. Nonlinear Regression Analysis and its Applications. Wiley, New York. Bennett, R.S., Etterson, M.A., 2006. Scholarly review: estimating pesticide effects on fecundity rates of wild birds using current laboratory reproduction tests. Hum. Ecol. Risk Assess. 12, 763–781. Bennett, R.S., Dewhurst, I.C., Fairbrother, A., Hart, A.D.M., Hooper, M.J., Leopold, A., Mineau, P., Mortensen, S.R., Shore, R.F., Springer, T.A., 2005. A new interpretation of avian and mammalian reproduction toxicity test data in ecological risk assessment. Ecotox 14, 801–815. Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference: a Practical Information–Theoretic Approach. Springer, New York. Cairns, J., 1993. Will there ever be a field of landscape toxicology. Environ. Toxicol. Chem. 12, 609–610. Chang, H.S., Anway, M.D., Rekow, R., Skinner, M.K., 2006. Transgenerational epigenetic imprinting of the male germ line by endocrine disruptor exposure during gonadal sex determination. Endocrinology 147, 5524–5541.

Crocker, D.R., 2005. Estimating the exposure of birds and mammals to pesticides in long-term risk assessments. Ecotoxicology 14, 833–851. Crocker, D.R., Hart, A.D.M., Gurney, J., McCoy, C., 2002. Estimating daily food intake of wild birds and mammals. Available from /www.pesticides.gov.uk/general/ ResearchReports/index.htm, http://ec.europa.eu/food/plant/protection/evaluation/guidance/wrkdoc19_en.pdfS, Appendix I. Etterson, M.A., Bennett, R.S., 2006. On the use of published demographic data for population-level risk assessment in birds. Hum. Ecol. Risk Assess. 12, 1074–1093. European Commission, 2000. Guidance Document on Terrestrial Ecotoxicology. Directorate General for Agriculture. Evans, D.M., Redpath, S.M., Elston, D.A., Evans, S.A., Mitchell, R.J., Dennis, P., 2006. To graze or not to graze? Sheep, voles, forestry and nature conservation in the British uplands. J. Appl. Ecol. 43, 499–505. Fischer, D.L., 2005. Accounting for differing exposure patterns between laboratory tests and field in the assessment of long-term risks of pesticides to terrestrial vertebrates. Ecotoxicology 14, 853–863. FOCUS, 2001. FOCUS surface water scenarios in the EU evaluation process under 91/414/EEC. Report of the FOCUS working group on surface water scenarios, EC Document Reference SANCO/4802/2001-rev 2. Forbes, V.E., Calow, P., 1999. Is the per capita rate of increase a good measure of population-level effects in ecotoxicology? Environ. Toxicol. Chem. 18, 1544–1556. Grasman, K.A., Scanlon, P.F., Fox, G.A., 1998. Reproductive and physiological effects of environmental contaminants in fish-eating birds of the Great Lakes: a review of historical trends. Environ. Monit. Assess. 53, 117–143. Gribbs, P.E., Pascope, P.L., Bryan, G.W., 1991. Tributyltin-induced imposex in stenoglossan gastropods—pathological effects on the female reproductive system. Comp. Biochem. Phys. C 100, 231–235. Hansson, L., 1971. Habitat, food and population dynamics of the field vole Microtus agrestis (L.) in south Sweden. Viltrevy, Swedish Wildlife, Svenska Ja¨gerfo¨rbundet (pp. 267–378). Hansson, L., 1977. Spatial dynamics of field voles Microtus agrestis in heterogeneous landscapes. Oikos 29, 593–644. Jensen, T.S., Hansen, T.S., 2001. Effekten af husdyrgræsning pa˚ sma˚pattedyr. In: Pedersen, L.B., Buttenschøn, R., Jensen, T.S. (Eds.), Græsning pa˚ ekstensivt drevne naturarealer—effekter pa˚ stofkredsløb og naturindhold. Park- og Landskabsserien nr, Vol. 34. Skov og Landskab, Hørsholm, pp. 107–121. Johnson, A.R., 2002. Landscape ecotoxicology and assessment of risk at multiple scales. Hum. Ecol. Risk Assess. 8, 127–146. Mineau, P., 2005. A review and analysis of study endpoints relevant to the assessment of ‘‘long term’’ pesticide toxicity in avian and mammalian wildlife. Ecotoxicology 14, 775–799. Myllima¨ki, A., 1977. Demographic mechanism in the fluctuating populations of the field vole (Microtus agrestis). Oikos 29, 468–493. Odderskær, P., Topping, C.J., Petersen, M.B., Rasmussen, J., Dalgaard, T., Erlandsen, M., 2006. Ukrudtsstriglingens effekter pa˚ dyr, planter og ressourceforbrug. Miljøstyrelsen.-Bekæmpelsesmiddelforskning fra Miljøstyrelsen 105, 127s Available from /http://www2.mst.dk/Udgiv/publikationer/2006/87-7052343-6/pdf/87-7052-344-4.pdfS. Ostby, J., Monosson, E., Kelce, W.R., Gray Jr., E., 1999. Environmental antiandrogens: low doses of the fungicide vinclozolin alter sexual differentiation of the male rat. Toxicol. Ind. Health 15, 48–64. Pastorok, R.A., Akc-akaya, R., Regan, H., Ferson, S., Bartell, S.M., 2003. Role of ecological modelling in risk assessment. Hum. Ecol. Risk Assess. 9, 939–972. Pertoldi, C., Topping, C.J., 2004. Impact assessment predicted by means of genetic agent-based modelling. Crit. Rev. Toxicol. 34, 487–498. Schmidt, N.M., Olsen, H., Bildsøe, M., Sluydts, V., Leirs, H., 2005. Effects of grassing intensity on small mammal population ecology in wet meadows. Basic Appl. Ecol. 6, 57–66. Sibly, R.M., Akc- akaya, H.R., Topping, C.J., O’Connor, R.J., 2005. Population-level assessment of risks of pesticides to birds and mammals in the UK. Ecotoxicology 14, 863–876. Tomlin, C.D.S., 1997. The Pesticide Manual, eleventh ed. British Crop Protection Council (pp. 1267–1268). Topping, C.J., Hansen, T.S., Jensen, T.S., Jepsen, J.U., Nikolajsen, F., Odderskær, P., 2003. ALMaSS, an agent-based model for animals in temperate European landscapes. Ecol. Model. 167, 65–82. Topping, C.J., Sibly, R.M., Akc- akaya, H.R., Smith, G.C., Crocker, D.R., 2005. Risk assessment of UK skylark populations using life-history and individual-based landscape models. Ecotoxicology 14, 925–936. Veeramachaneni, D.N.R., Palmer, J.S., Amann, R.P., Kane, C.M., Higuchi, T.T., Pau, K.Y.F., 2006. Disruption of sexual function, FSH secretion, and spermatogenesis in rabbits following developmental exposure to vinclozolin, a fungicide. Soc. Rep. Fer. 131, 805–816. Walker, C.H., 2006. Review: ecotoxicity testing of chemicals with particular reference to pesticides. Pest. Manage. Sci. 62, 571–583. Walker, C.H., Hamilton, G.A., Harrison, R.B., 1967. Organochlorine insecticide residues in wild birds in Britain. J. Sci. Food Agric. 18, 123–129. Whitehead, R., 2000. The UK Pesticide Guide 2000. British Crop Protection Council and CABI Publishing (pp. 555–556).