Demography and Modeling To Improve Pesticide Risk Assessment of

Chapter 18

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Demography and Modeling To Improve Pesticide Risk Assessment of Endangered Species John D. Stark* Washington State University, Puyallup Research and Extension Center, 2606 W Pioneer, Puyallup, WA 98371 *E-mail: [email protected]

The present ecological risk assessment process for pesticides as practiced by the United States Environmental Protection Agency under the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) consists of developing short-term toxicity data for a few select species and comparing these data to expected environmental concentrations to develop risk quotients (RQ). Risk quotients are then compared to “levels of concern” (LOC) which vary depending upon the type of pesticide being evaluated and the type of organisms likely to be exposed. The LOC is supposed to account for all of the types of uncertainty associated with the risk assessment. There are several problems associated with this process. For example, populations do not respond the same way to toxicant exposures as do individuals. Populations, if thinned may compensate for losses and actually grow faster than expected. Furthermore, exposure to pesticides can result in a proportion of a population dying while the remaining individuals are impaired due to multiple sublethal effects. Another issue is that the few species that are used for the development of toxicity data may not be good representatives of the many species they are supposed to protect. These species are often chosen based on ease of rearing and evaluation, not because they are good representatives of many species. Susceptibility of a population to pesticides is influenced by life history traits and differences in life history traits are not considered in the current risk assessment process.

© 2012 American Chemical Society In Pesticide Regulation and the Endangered Species Act; Racke, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2012.


It has also been shown that populations of organisms often exist as mixtures of life stages. The makeup of the population structure can greatly influence susceptibility to pesticides and this is also not considered in the risk assessment process. Furthermore, different life stages may exhibit vastly different susceptibility to toxicants, yet usually only one life stage is evaluated. In this chapter, the current risk assessment process and new approaches to improve risk assessment of pesticides are discussed. The new approach consists of developing population-level measures of toxicant effect that incorporate the total effect (lethal and multiple sublethal effects) followed by population modeling to determine the probability that specific concentrations of pesticides will drive populations to extirpation or whether populations will recover.

Introduction The pesticide industry is a large and important industry with an estimated value of $39.4 billion worldwide in 2007 (1). There are great benefits to the use of pesticides, particularly with regard to food production, protection of homes and other structures, as well as protection of human and animal health. The reduction of malaria transmission alone saves many lives each year. The down-side to pesticide use is the potential to have negative impacts on non-target organisms, including humans. By definition, pesticides are designed to kill pest species. Because pest species share similar, if not identical physiological systems with other organisms, it is impossible to not affect certain non-pest species if they are exposed (2, 3). Therefore, there are risks associated with the use of pesticides. In this chapter, the focus will be on how the United States Environmental Protection Agency (EPA) develops ecological risk assessments for pesticides. The problems associated with the EPA process will be discussed and a new approach involving population-level estimates of effect followed by population modeling will be outlined that may improve our ability to estimate potential pesticide risks.

The EPA Risk Assessment Process The ecological risk assessment process for pesticides is used to estimate the potential risk that pesticides might have on non-target species, including endangered species (4–6). The approach used by the EPA involves a comparison of toxicity endpoints developed for a select number of species to an estimated environmental concentration. The toxicity endpoints are measured in individuals and are almost always measures of acute mortality or effects on reproduction. The Expected Environmental Concentration (EEC) is estimated through modeling (5, 6). A risk quotient (RQ) is developed by dividing the EEC by the toxicity endpoint. The following equation is used for the development of a risk quotient: RQ = EEC/LC50 260 In Pesticide Regulation and the Endangered Species Act; Racke, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2012.

Table I. EPA risk presumptions for ecological risk assessment of pesticides Risk Presumptions and LOCs Risk Presumption

RQ

LOC

Birds1 Acute Risk

EEC/LC50 or LD50/sqft or LD50/day

0.5

Acute Restricted Use

EEC/LC50 or LD50/sqft or LD50/day (or LD50 < 50 mg/kg)

0.2

Acute Endangered Species


0.1

Chronic Risk

EEC/NOEC

1


Wild Mammals1 Acute Risk


0.5


EEC/LC50 or LD50/sqft or LD50/day (or LD50 < 50 mg/kg)

0.2



0.1

Chronic Risk

EEC/NOEC

1

Aquatic Animals

2

Acute Risk

EEC/LC50 or EC50

0.5


EEC/LC50 or EC50

0.1


EEC/LC50 or EC50

0.05

Chronic Risk

EEC/NOEC

1

Terrestrial and Semi-Aquatic Plants Acute Risk

EEC/EC25

1


EEC/EC05 or NOEC

1

Acute Risk

EEC/EC50

1


EEC/EC05 or NOEC

1

Aquatic Plants2

LD50/sqft = (mg/sqft) / (LD50 * wt. of animal) LD50/day = (mg of toxicant consumed/day) / (LD50 * wt. of animal) 2 EEC = (ppm or ppb) in water.

1

The LOC is used to account for uncertainty in the risk quotient. Uncertainty stems from many sources. For example, data is only developed for a small number of species that are used to represent many species. Data developed for Bobwhite quail and mallard ducks are used to represent all birds and reptiles. Data for a few fish species are used to represent all fish and amphibians. The honey bee is used to represent all insects. Other sources of uncertainty include differences in susceptibility among other unstudied organisms, individual-to-population-level extrapolation, laboratory-to-field extrapolation, variability among the data, and uncertainty in the actual amounts of pesticides in ecosystems. The LOC varies 261 In Pesticide Regulation and the Endangered Species Act; Racke, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2012.


depending upon the type of pesticide being evaluated as well as the type of organism potentially exposed (Table I) (7). If the LOC is exceeded, then action(s) must be taken to reduce the risk. These actions may include the development of additional data or changes in the amounts of pesticides applied, frequency of applications, and where they are applied. The toxicity data for pesticides is developed for a standard group of organisms. These organisms are supposed to be representative of different functional groups in an ecosystem, but are chosen based on the ease with which they can be reared and maintained in the laboratory. The data required for the registration of a pesticide in the United States can be found via the EPA web site (URL http://www.epa.gov/pesticides). For example, the terrestrial and aquatic non-target data requirements for a pesticide applied outdoors are: • • • • • • • • •

Two avian oral LD50 studies Two avian dietary LC50 studies Two avian reproduction studies Two freshwater fish LC50 studies One freshwater invertebrate EC50 study One honeybee acute contact LD50 study One freshwater fish early-life stage study One freshwater invertebrate life cycle study Three estuarine acute LC50/EC50 studies -- fish, mollusk and invertebrate

It is important to point out that the freshwater invertebrate life cycle test listed above is not a demographic study (see below) but instead is a longer term reproduction study, for example the 21 day Ceriodaphnia dubia test (8).

Problems with the EPA Risk Assessment Process The ecological risk assessment process for pesticides as practiced by the EPA has several short-comings. Some of these issues are discussed below. Multiple Toxic Effects The EPA approach does not take into account the “total effect” of a pesticide. Exposure to pesticides can result in a proportion of a population dying while the remaining individuals are impaired due to multiple sublethal effects. In other words, the survivors may have a reduced life span, behavioral changes that reduce their ability to find food, home to breeding grounds, and find a mate. Thus, not one but several sublethal effects may occur in individuals exposed to pesticides. This results in a population that is much more susceptible than predicted by measurements of acute mortality and reproduction only. Therefore, what happens to individuals does not necessarily translate to what happens at the population level. Stark (9) published examples of populations being more or less susceptible than predicted by individual measures of effect. Stark (9) exposed populations 262 In Pesticide Regulation and the Endangered Species Act; Racke, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2012.

of the water flea, Daphnia pulex, to the acute LC50 for several pesticides and adjuvants and let the populations grow and reproduce. At the end of the study, population size was recorded. None of the populations were 50% smaller than the control. The majority of the populations had gone to extinction and the population exposed to diazinon was 91% of the control, which was much higher than predicted.


Individuals Versus Populations With the current ecological risk assessment process, individuals are evaluated with toxicity tests. However, populations do not respond the same way to toxicant exposures as do individuals (10). Populations, if thinned may compensate for losses and actually grow faster than expected. This process is called “population compensation” (11). The Surrogate Species Issue It is obvious that toxicity data cannot be developed for all species. Therefore, surrogate species must be evaluated and data for these few select species is used to protect all others. Furthermore, because of the “take” clause in the Endangered Species Act (ESA) (12) threatened and endangered species cannot be tested in the Unites States. The problem with surrogate species is that no one knows whether the species chosen as representatives are actually good predictors of pesticide impact on the vast majority of other species. The species that are evaluated have quite often been chosen based on their ease of rearing in the laboratory and not because they represent an average species or a protective species. Two issues come into play with surrogate species. The first is that different, even closely related species can exhibit vast differences in susceptibility to pesticides (see below for a discussion of this topic). The second issue is that species have developed different strategies that maximize their survival (13). Some species have long lives, produce few offspring, and make a major investment in nurturing their offspring (e.g., humans). Other species do not nurture their young, have short life-spans, and produce large numbers of offspring often throughout their life span. These differences in life history traits can have a large impact on susceptibility of a population to pesticides. One way to envision this is to consider what I have called the “rat-elephant phenomenon”. This phenomenon is quite simplistic and is best understood with the following example. If we have two populations, one consisting of 100 rats and the other consisting of 100 elephants and you kill 50% of each population, which population should recover the fastest to reach its initial population size of 100? The answer, obviously, is the rat population. Rats will recover much faster than elephants. The reason for this difference in population recovery is that the rat population reproduces at a much younger age, and produces many more offspring and broods of offspring than the elephant population. This is obviously an extreme example, but differences in life history strategies have implications for ecological risk assessment of pesticides. As mentioned above, uncertainty factors (LOC) are used to account for many types of uncertainty, including differences in life histories. However, how can we be 263 In Pesticide Regulation and the Endangered Species Act; Racke, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2012.


sure that use of toxicity data developed for one species (e.g., Bobwhite quail) is protective of all bird and reptile species when these species have a wide range of life history strategies? Of course the rat-elephant example is extreme and a risk assessor is much more likely to compare toxicity data developed for a rainbow trout to protect endangered salmon species. However, Banks et al. (14) evaluated several fish species used to develop toxicity data as surrogates to protect Pacific salmon. Interestingly, the species most closely related to anadromous salmon in this study, the cutthroat trout, was one of the least protective species; the Round Goby was the most protective. In an attempt to come up with protective fish models, Hanson and Stark (15) have developed an average and a protective fish model. Furthermore, Stark et al. (16, 17) evaluated the response of closely related arthropod species to the same levels of stress (mortality, reductions in the number of viable offspring, or a combination of both of these factors). They found that populations of these species recovered at very different rates indicating that use of one species to protect others is risky. Population Structure It has also been shown that populations of organisms often exist as mixtures of life stages. The structure of a population can greatly influence susceptibility to pesticides and this is not considered in the risk assessment process. Stark and Banken (18) evaluated two arthropod species, the two-spotted spider mite, Tetranychus urticae (Koch), and the pea aphid, Acrythosiphon pisum (Harris), with different starting population structures to determine whether different population structures would influence population-level susceptibility to pesticides. The three differently structured populations evaluated were (1) eggs or neonates for A. pisum and T. urticae, respectively, (2) stable age distribution, and (3) young adult females only. Population growth rate was the endpoint of interest in this study for both unexposed and pesticide-exposed populations. Populations of T. urticae were exposed to 100 µg/l of the pesticide dicofol while the populations of A. pisum were exposed to 200 µg/l azadiracthin, the active ingredient in Neemix. Population growth rate for the three control populations in a closed system converged on days 16 and 17 days after the start of the study, for T. urticae and A. pisum, respectively. However, population growth rate for populations of T. urticae and A. pisum started as eggs of neonates were significantly lower than populations with the adult and mixed-age populations. These results indicate that the population structure of a population has a significant influence on the impact of pesticides. Differential Susceptibility As mentioned above, even closely related species can exhibit vastly different susceptibilities to toxicants. An example of how different susceptibility can be was presented by Deardorff and Stark (19). They determined the acute toxicity of the insecticide, spinosad to three species of Water fleas (Cladocerans), Daphnia pulex, D. magna and Ceriodaphnia dubia. Their results showed that C. dubia was 72 times more susceptible than D. pulex to spinosad. 264 In Pesticide Regulation and the Endangered Species Act; Racke, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2012.


Does the Risk Quotient-Level of Concern Approach Work? The obvious question after this discussion is: “Does the Risk Quotient-Level of Concern method protect species”? Little work has been done to answer this question. We have found that in some cases the method is over-protective and in other cases it is under-protective. Hanson and Stark (20) evaluated the Toxicity Exposure Ratio (TER), the analogous method to the risk quotient used in the European Union, for Daphnia pulex exposed to the insecticide spinosad. They found that the TER was overprotective by a factor of 6 for D. pulex. In other studies we have found that the RQ-LOC does not provide protection of certain species when they are evaluated at the population level using the demographic toxicity approach (see below). For example, Chen et al. (21) evaluated the effects of the insecticide imidacloprid on the water flea, Ceriodaphnia dubia. The EPA EEC for imidacloprid in surface water systems is 17.4 µg/l and this concentration is considered safe for aquatic organisms. However, Chen et al. (21) found that exposure of population of C. dubia to an imidacloprid concentration of 0.3 µg/l, a concentration that is well below the EEC, resulted in a 27% reduction in population size. Therefore, a population approach using demographic parameters proved that the current risk assessment process may not work.

How Do We Improve the Ecological Risk Assessment Process? The above mentioned issues that are problematic with the current ecological risk assessment process for pesticides can be dealt with if actual populations exposed to pesticides are monitored over longer periods of time where reproduction occurs. One way to do this is with demographic toxicity studies followed by population modeling. Demography and Modeling Demography is the study of populations and the processes that shape them (22, 23). Life tables are a major component of demography and are usually developed from detailed measures of individual survival and reproduction. Life tables have been used by the life insurance industry to determine the probability that a person will die and by ecologists to develop information on basic population biology. A number of population parameters are derived with life tables. The most important of these is the population growth rate which comes in two forms, the intrinsic rate of increase (rm) (the rate of natural increase in a closed population) and lambda (λ) (the population multiplication rate) which is the anti-log of rm. Growth rates are important because they tell us about the health of a population. A lambda value of 1 indicates that a population is stable (neither increasing or declining) a growth rate greater than 1 indicates that the population is increasing exponentially, and a growth rate less than 1 indicates that a population is declining and heading towards extinction. Other demographic parameters developed in a life table are the net reproductive rate (R0) (the per generation contribution of newborn females to the next generation), the intrinsic birth rate (b) (the per capita instantaneous 265 In Pesticide Regulation and the Endangered Species Act; Racke, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2012.


rate of birth in the stable population), intrinsic rate of death (d) (the per capita instantaneous rate of death in the stable population), doubling time (DT), the time it takes a population to double, and generation time (T), the time required for a newborn female to replace herself Ro-fold. Demographic data can be developed for unexposed and pesticide-exposed populations. These data have a great advantage over traditional toxicity data which is developed as single measures of effect in individuals. The advantage is that a measure of total effect (lethal and multiple sublethal) at the population level is obtained. Studies using demography to evaluate the effects of toxicants on populations have been published for a long time, but this approach has still not been widely used by environmental toxicologists (24–30). Another advantage of demographic toxicity data is that it can easily be incorporated into more sophisticated mathematical models that enable one to analyze population outcomes over time (31).

Matrix Models Several types of population models have been developed that can be used to interpret effects of toxicants on populations (31, 32). However, matrix models which are based on linear algebra theory are the most commonly used by ecologists and population biologists (30, 33, 34). Matrix models have been used to make management decisions to protect threatened and endangered species (35–37). Matrix models have also been used in the past to estimate the impact of toxicants on populations (38–40). Matrix models are simple to construct and understand. The data required for their development are probability of survival, reproduction, and whether individuals remain in a stage or age class or move to the next one. These data may be obtained from laboratory studies or from observations of populations in the field. A typical matrix equation is presented below in equation . Survivorship values (P) are placed on the subdiagonal while fecundity values (F) are placed along the top of the matrix. The vector, n(t), represents the starting condition of the population and consists of numbers of individuals in each stage or age category (n1, n2, n3 etc.). Equation 1. Matrix model example

266 In Pesticide Regulation and the Endangered Species Act; Racke, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2012.

An example of how a matrix model works is presented below. In this example, the initial vector at the beginning of a study is a stage-structured population consisting of three stages, eggs, juveniles, and adults. The starting total number of individuals in the population is 24. The survivorship values (shown on the diagonal) show that 100% of the eggs hatch but that only 50% develop into juveniles. Additionally, the fecundity values (on the top line) show that the eggs and juveniles do not reproduce (both zeros) but that 12 offspring are produced by the adults. This species reproduces once per year and therefore the time step is one year.


A matrix multiplication example is shown below in Table II:

Table II x

New Vector (Nt + 1)

00 00 12

12x0 8x0 4x12

48

8

01 00 00

12x0 8x0 4x12

12

Adult

4

00 0.5 00

12x0 8x0 4x12

4

Total No. of Individuals

24

Starting Vector (Nt)

Egg

12

Juvenile

Matrix

=

Multiplication

Stage

64

After one time step (multiplication), the population consists of 64 individuals and we now have a new vector (Nt+1). This new vector is then multiplied against the matrix to obtain a third vector (Nt+2) and this process is continued projecting the population into the future. After a number of multiplications, the proportion of individuals in each stage will become a constant. It is at this time that the stable age distribution is reached. This type of matrix model is called a deterministic model. It does not take into account stochastisity or random variation. Both deterministic and stochastic matrix models can be developed and serve different purposes. Deterministic models are often used to develop population growth rates and conduct elasticity analysis. Elasticity analysis is used to show which life stage is the most sensitive with regard to contribution to population fitness (36, 41–43). Stochastic models are much more complicated and are often developed for use as decision models for the protection of threatened and endangered species (34, 36). An Example of a Deterministic Population Model Using the example above, where we have a hypothetical species with 12 eggs, 8 juveniles and 4 adults, deterministic matrix models were constructed where the first population is an unexposed control. The second population has been exposed once yearly to a pesticide and the only effect is 25% mortality. A third population 267 In Pesticide Regulation and the Endangered Species Act; Racke, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2012.


has been exposed to a pesticide yearly and 25% of the population dies while the remaining 75% produce 25% fewer offspring than the control. Again the time step is one year and the model was run for 10 years. In this example, we see that the control population, which started as 24 individuals, increased to 13,632 individuals after 10 years (Fig. 1). The population that had a 25% mortality rate grows to 1,527 individuals over 10 years, and the population that is reduced 25% and has a 25% reduction in offspring only grows to 1,000 individuals after 10 years. Therefore, the population that experienced 25% mortality was actually reduced 89% and the population that experienced 25% mortality and a 25% reduction in offspring was reduced 89% compared to the control population over a 10 year period. Clearly, these types of results cannot be obtained using the risk quotient-LOC method.

Figure 1. Population trajectories for a hypothetical species unexposed (control) and exposed to a pesticide that either causes mortality only or mortality and a reduction in fecundity.

Conclusions The ecological risk assessment process used by the EPA for pesticides involves a comparison of an expected environmental concentration to a toxicity endpoint leading to development of a risk quotient. Risk quotients are developed for a few select species. The risk quotient is then compared to a level of concern which varies depending on the type of pesticide being evaluated and the species of concern. Levels of concern are supposed to take in account all of the uncertainty associated with the risk assessment process. The current risk assessment process 268 In Pesticide Regulation and the Endangered Species Act; Racke, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2012.

does not consider population-level processes, differences in life history traits, multiple effects (lethal and sublethal), population structure and other issues. Demographic toxicity data gives a complete picture of the total effect of pesticides on populations. These data can be used in various population models to determine when and if populations will recover from exposure to pesticides and the probability of extinction. Use of demographic data and population modeling should improve our ability to protect the environment.

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Demography and Modeling To Improve Pesticide Risk Assessment of

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