PC BEEPOP, an ecotoxicological simulation model for honey bee populations

Environmenlal Toxicology and Chemrdry, Voi 10, pp 547-558, 1991 Printed In the USA. Pergamon Press pic

0730-7268/9i $3 00 + 00 Copyright 0 1991 SETAC

Hazard Assessment

PC BEEPOP, AN ECOTOXICOLOGICAL SIMULATION MODEL FOR HONEY BEE POPULATIONS

JERRY J. BROMENSHENK,* JIRI DOSKOCIL and GERALD J. OLBU Division of Biological Sciences, University of Montana, Missoula, Montana 59812

GLORIA DEGRANDI-HOFFMAN and STEPHAN A. ROTH Agricultural Research Service, U.S. Department of Agriculture,

Carl Hayden Bee Research Center, 2000 East Allen Road, Tucson, Arizona 85719

(Received 12 September 1989; Accepted 24 Jub 1990)

Abstract-PC BEEPOP is a computer model that simulates honey bee (Apis mellifera L.) colony population dynamics. The model consists of a feedback system of interdependent elements, including colony condition (e.g., initial size, reproductive potential of the queen and forager longevity), environmental variability (e.g., weather) and contaminant exposures. It includes a mortality module (BEEKILL) and a chemical-toxicity data base and probit analysis program (BEETOX). PC BEEPOP is a modified version of an existing colony dynamics model, BEEPOP, and can be used as a tool for environmental risk assessments. Results of sensitivity analysis and simulations of various climatic and toxic scenarios are discussed and compared to observed changes in the size and composition of honey bee colony populations.

Keywords- Populations Chemical toxicity

INTRODUCTION

Assessing ecological risks can be a complicated process. Because many factors can influence the results, it is often difficult to establish cause and effect. As Emlen and Pikitch [ l ] noted, efforts to “relate lethal and sublethal effects on individuals to changes in population dynamics” have lately been attracting attention. Models place variables in the control of the user, which helps clarify the out- come. One such model is BEEPOP, recently developed and tested by DeCrandi-Hoffman et al. [2].

BEEPOP is a mainframe computer simulation model of the population dynamics of a honey bee colony. We rewrote BEEPOP for IBM-compatible computers (PC BEEPOP) and added BEETOX, a toxicology dose-response data base for more than 400 chemicals. BEETOX calculates both mortality for specific exposures and exposures for specific mortality. Then we provided the model with subroutines such as BEEKILL to assess bee deaths from natural and human-induced causes.

PC BEEPOP is intended to provide reasonably simple, but nonetheless realistic, predictions of bee population responses to environmental variability (e.g., weather conditions) and contaminant

*To whom correspondence may be addressed.

Hazard assessment

exposure. The PC’s compatibility, pull-down color menus, on-line help, automatic error checking, and screen graphics make the model readily accessible and user friendly. The user has immediate access to information about honey bee biology and results of field and laboratory toxicity testing. Because PC BEEPOP requires only minimal levels of technical expertise, almost anyone can use it. P C BEEPOP is designed primarily for technical applications, including applied research and ecological assessments of hazardous waste sites, large geographical areas, and pesticide application.

P C BEEPOP uses a feedback system of interdependent elements to consider climate, population, pathogen, and pollution interactions (Fig. 1). The model derives its parameter values from published literature and consists of two main routines [ 2 ] . The first subroutine determines the number of eggs laid by the queen at any time ( t ) and the percentage that will become workers (females from fertilized eggs) and drones (males from unfertilized eggs). The second is the bookkeeping routine that tracks daily development of eggs to adults. P C BEEPOP assumes that enough plants are bloom- ing to provide sufficient nectar and pollen to sus- tain the colony.

The results of field and laboratory testing in

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548 J. J . BROMENSHENK ET AL.

I F‘C BEEPOP I

D E A D D R O N E S

Fig. 1 . Logical flow of the components of the PC BEEPOP model. BEEKILL can modify any or all life stages (enclosed by dashed border).

combination with the BEETOX data base and the PC BEEPOP population model can be used for a variety of assessment purposes ranging from ranking of sites according to toxicity, relating lethal and sublethal effects on individuals to changes in population dynamics, discovering which demographic parameters are critical, identifying underlying uncertainties, and examining population responses in the context of environmental variability (e.g., altered temperature) (and natural factors (e.g., disease and predation).

Honey bees are an excellent subject for risk assessments. They are ecologically and economically important as essential pollinators of many U.S. crops [3] and as producers of honey, wax, and other products. Bees are ubiquitous and are kept in cities as well as rural areas. The same species occurs worldwide. In addition, three to four million managed colonies across the United States offer an in-place monitoring network.

Bees can monitoir distribution and impacts (re- viewed by [4,5]) of many environmental contami- nants over large geographical areas [6-81. The U.S. Environmental Protection Agency (EPA) classifies

the use of bees for terrestrial toxicity bioassays and in situ assessments of hazardous waste sites as a Class I, off-the-shelf, method [9 ] . “Standardized” test protocols for honey bees exist due to the use of bees as biomonitors [4-71 and to testing for hazards to pollinators required by the Federal Insec- ticide, Fungicide and Rodenticide Act (FIFRA).

Honey bee colonies are multidimensional test systems. They can be sampled to monitor exposures to toxic chemicals via bioaccumulation. Mea- surements of the same colony can reveal lethal effects (e.g., mortality), sublethal effects (e.g., shortened life span or responses of biomarkers such as metallothionein and acetylcholinesterase), and behavioral effects (e.g., disrupted foraging and housecleaning). In addition, toxicity bioassays can be conducted in the field or laboratory. Inferences can be made to the ecosystem level via the pollina- tion syndrome.

Bees may seem unlikely and difficult to manage as test organisms for ecological assessments. But miniature or disposable hives and the technical support readily available from bee research labo- ratories, beekeepers, and state and federal agencies make them an inexpensive and practically self- sustaining test system [4-6,101.

Although bee colonies are complex organiza- tions, one individual (the queen) responds to nearly all of the factors that alter population size and age- specific composition. A colony’s population size and composition depend on how many eggs the queen can lay and how many bees can be reared to adulthood [2].

The original BEEPOP simulated the effects of weather and various hive parameters on brood- rearing cycles and demonstrated that the cycles vary with temperature and geographic location. All parameters tested by sensitivity analysis (including weather, initial population size, the queen’s egg- laying potential, spermatozoa stores, and forager life span) affected population growth and age structure [2].

The simulations did not consider other causes of colony attrition. In our current study, we added subroutines and conducted further simulations to address those factors. We simulated responses of new and established colonies, various age distributions, acute and chronic toxicity, brood developmental rates and forager longevity, and changing weather. We also compared model estimates to measured responses of colonies placed along a gradient of exposure to heavy metals from industrial sources.

PC BEEPOP: Honey bee ecotoxicological model 449

METHODS

We wrote PC BEEPOP in FORTRAN as an ex- ecutable file for IBM-compatible computers. A complete description of the model, literature cita- tions for model assumptions, and the underlying equations appear in DeGrandi-Hoffman et al. [2].

P C BEEPOP primarily is a bookkeeping model. It estimates the numbers of eggs, larvae, pupae, hive bees, and forager bees for every day of the year by considering weather variables, foraging, and density-dependent factors (i.e., number of bees).

BEEPOP can readily simulate the population dynamics of honey bees because their developmental stages from egg to older adult follow a relatively stable and predictable sequence of events. For example, an egg usually becomes a worker hive bee in 21 d and a forager bee in 42 d [ l 11. In the model, normal worker life expectancy depends on foraging, which varies with season of emergence. The default value for forager life span is based on Neukirch [ 121, who reported that a"physiologica1" clock limits forager flight activity to about 800 km or 10 d. PC BEEPOP only allows foraging on days when the average temperature is >12"C [13], wind speed <34 km/h [14], and rainfall <0.5 cm/d [2]. Hours of sunlight and degree-days interact with population size to govern the actual number of eggs laid per day. Removal events such as exposure to toxic chemicals, disease, or swarming are sub- tractive processes that the model incorporates in its bookkeeping calculations.

Simulations begin by choosing the type of colony (established or new), colony size, and simulation time frame. The user may change any of more than 100 input variables, grouped under pull-down menus for WEATHER, COLONY, STRESS, TOXICS, and OPTIONS. Simulations can be run without changing any variables, as default values are provided for all inputs.

Most simulations will include selection of a weather file for the site to be assessed. WEATHER files are ASCII records of photoperiod, rainfall, wind speed, and minimum and maximum temperature for each day of the year.

COLONY alters factors affecting the queen, workers, drones, and all age categories. For example, the submenu QUEEN inputs such information as the queen's egg-laying potential, number of days she has been laying, number of spermatozoa used per fertilized egg, and amount of store spermatozoa. Submenus WORKER and DRONE alter the

length of brood, hive and forager stages. RATIO and SPACE affect brood rearing. Ratios of adults to brood change with colony growth and the season of the year. Default rations are 2: l for spring, 1 : 1 for summer, and 1 :2 for autumn [ 151. Brood nest space varies with size of the hive [16] and food stores (i.e., cells used for food storage are unavail- able for brood). SWARM simulates swarming and supersedure, events that remove a queen and masses of adult bees.

STRESS includes NATURAL, which simulates miscellaneous constant, low-level losses. In DIS- EASE, an exponential equation with user-modifiable coefficients simulates increasingly high death rates. WINTER adjusts for a user-specified population attrition during a season when egg-laying and foraging are suspended. Winterkills in north- ern temperate areas range from 5 to 3Ovo of the total number of colonies and up to two-thirds of the bees in surviving colonies [l 11.

TOXICS includes the BEETOX toxicology data base. BEEKILL, composed of LONGEVITY and MORTALITY, uses overlays to reduce longevity or to kill bees outright. LONGEVITY decreases adult life span (hive bees and forager bees) by a specified number of days. MORTALITY specifies percentage of mortality, developmental stages affected, and initial and ending dates of the toxic episode. A simulation can cover short-term, acute events (e.g., a 1- to 2-d pesticide kill) and long-term, chronic events (e.g., continuous exposure to toxics at a waste site), and can include up to nine different mortality events (e.g., spray programs that occur repeatedly during the growing season).

On the day the toxic event begins, BEEKILL se- lectively removes the specified percentages of individuals for each day of age within the chosen life stages. On subsequent days, as long as the chemical is present, BEEKILL removes a similar percentage of bees entering the affected life stage.

Survivors of the first exposure are assumed to be somewhat resistant to toxins and subsequently are no longer figured into the initial mortality rate. To simulate residual mortality for this group, the user can select a decay equation that reduces mortality through time. Currently the model uses an exponential equation with modifiable coefficients. Addition of other equations, such as a binomial function, is planned.

OPTIONS customizes the simulation. P C BEE- P O P uses a randomization equation (RANDOM) to assure that when the simulation begins, each developmental stage includes bees of different ages.

5% J. J. BROMENSHENK ET AL.

The user can override the randomizing routine and manually (MANUAL) assign a specific number of individuals to each life stage and day of age. WEATHER allows increasing or decreasing of average daily temperatures.

P C BEEPOP outputs daily values for the number of eggs, larvae, pupae, hive bees, forager bees, total adult workers, total adult drones, total adult population, and the amount of sperm used. This output can be displayed on the screen as a graph or table and can be sent to a printer or plotter.

Having developed P C BEEPOP, we conducted sensitivity analysis and simulations of various toxic events to assess their influence on the population. We simulated acute and chronic bee kills for new and established colonies, for each developmental stage, at various seasons, and in different climates (southwestern, miclwestern, and Pacific North- west). To determine PC BEEPOP’s sensitivity to altered weather, we edited the weather tapes. We added three weeks of rain (one each in spring, summer, and autumn, three of wind, and two of cold (spring and early summer). We also incremen- tally increased and decreased mean daily temperatures. Additional simulations, using the same initial parameters as in the preceding tests, assessed the influence of reducing forager life span compared to using reduced recruitment to simulate mortality.

To demonstrate that PC BEEPOP is at least consistent with real data, we compared data from model simulaJions to a field trial in which colonies were exposed to industrial emissions of As and Cd. The 43-d field study was conducted on Vashon Is- land, Washington, in 1984. Details are reported elsewhere (Bromenshenk, manuscript in prepara- tion). We deployed 50 minihives (one-fourth the size of standard hives) at five locations along a known pollution gradient, downwind from heavily industrialized Comrnencernent Bay (near Tacoma, WA). We weighed the hives and bees to determine initial and final mass, using a platform balance calibrated with weights traceable to the National Bureau of Standards. We estimated bee populations from weekly counts of frames covered by clustered bees, and we calibrated the counts by comparing them to measured mass of bees shaken from frames. Each week we made acetate tracings of sealed (pupae) and unsealed (eggs, larvae) cells and areas of stored nectar, honey, and pollen. We digitized the areas to determine the amount of brood. We determined exposure levels by residue analysis of forager bees aspirated from hive en- trances, following the procedures in [6].

RESULTS

Influence of life stage and age distribution New colonies can be started from a “package”

of bees, that is, a screen-box or package containing a new queen, adult workers, and no brood. Es- tablished colonies are defined as populations containing all life stages (e.g., eggs, larvae, pupae, hive bees, and foragers). Simulations of NEW ver- sus ESTABLISHED colonies displayed different population dynamics (Figs. 2a and 2e). New colonies demonstrated an oscillatory pattern of increase, whereas established colonies tended t o increase linearly and were more stable. Package bees reached smaller peak population sizes, but by fall were nearly equivalent in size to established colonies.

Randomization of the age distributions within each life stage introduced some variability, but generally had little effect on predicted growth curves. Based on repeated simulations ( n = 10) using different randomizations of age, coefficients of variation (C.V.s) ranged from about 15% to less than 2%. The higher C.V.s occurred during the initial days of each simulation.

Influence of acute and chronic toxicity on colony population size

Acute toxicity simulations consisted of killing 30% of the hive and forager bees for 2 d on the 15th of each month (Figs. 2d and 2h). Short-term, severe losses of adult bees in early to midsummer had the greatest influence on peak adult population size. Losses of adults in late summer or early autumn had a greater influence on the number of bees in the overwintering cluster. The time of the toxic episode governed the rate and degree of recovery.

Acute toxic events in the spring had highly variable effects, ranging from only a slight depression of numbers to extinction of the colony. Extinction occurred when adult numbers were low, such as in the spring just after brood production resumes. For every day of the year-long simulation, chronic toxicity (Figs. 2b, 2c, 2f and 2g) removed between 5 and 10% of the individuals entering a specific developmental stage (egg, larva, pupa, or adult), as well as 5% of the individuals entering all of the developmental stages (18.55% cumulative mortality).

For both acute and chronic toxicity, loss of adult bees had a greater effect than loss of other life stages. Egg mortality had the least effect. Low- level sustained toxicity often led to smaller wintering populations than did short-term mortality,

PC BEEPOP: Honey bee ecotoxicological model 55 1

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Fig. 2. Effects of climate, initial population conditions and toxic events on colony growth (a,e) Established or old

and all life stages (d, h) Acute 30% mortalities of adults in mid-June, mid-July and mid-September A 30% mor tality in mid-April (not shown) had little effect other than slightly lowering the entire growth curve

colonies (solid line) vs. new (dotted line) (b,f and c,g) In descending order, S % chronic mortalities of eggs, adults

552 J. J. BROMENSHENK ET AL.

unless the acute episode occurred late in the summer or early fall. Mortalities affecting more than one developmental stage had more than simply ad- ditive effects. Killing 5% of all new eggs, larvae, pupae, and adults (18.55% cumulative mortality) reduced peak populations by as much as 3 1 Yo.

Influence of weather factors Although climate strongly influenced growth

curves, short-term changes in weather only mod- erately affected them. Three weeks of extra rain increased the peak population under midwestern conditions by about 6%. Adding three weeks of wind had no discernilble effect, and two additional cold periods decreared the peak population by about 7%. Warmer weather (i.e., increasing average temperature over a year’s time) lengthened egg- laying and foraging periods and had a slight effect on peak population ske. Conversely, cooler weather reduced egg-laying and foraging.

Influence of rate of development and life span

Changing brood developmental rates or forager life span strongly influenced model predictions, especially over extended periods of time (Table 1). The effects were most pronounced for new (package) colonies. Changing the mean length of the brood cycle by 2 d (from 21 d-a 9.5% difference) over a year-long simulation resulted in a 1 to 38% alteration in peak population size, depending on climate, colony size and colony type. A 3-d change

in forager life span (from 10 d-a 30% difference) resulted in a 25 to 125% alteration in peak population.

Comparing BEEKILL-BEEPOP simulations to a field study

Figure 3a displays five-week growth curves for minicolonies in our field study. Site 1 was the closest to the heavy metal source, and Site 5 was farthest from it. In order of exposure, as indicated by tissue residue concentrations of As and Cd in forager bees, site ranks from high to low were 1 > 3 > 2 > 4 = 5 . Mean As content of bees from Site 1 was 13.9 ppm, compared to 4.2 ppm for Sites 4 and 5 . Mean Cd content was 4.07 ppm for Site 1 and 2.8 ppm for Sites 4 and 5 .

The growth curves in Figure 3a represent “best fit” regressions. Observations began just before the emerging brood began to replace the “old” adult population. Growth curves for the three low exposure sites (2, 4, and 5) were nearly linear. Curves for the two highest exposure sites (1 and 3) were not linear. The highest exposure site (1) was de- scribed by a second-order polynomial function; the second-highest exposure site (3) fluctuated more than any of the other sites.

Growth dynamics for all five sites were similar over the first 17 to 24 d. Each colony started with about 4,500 bees, then dropped to a low of 2,400 to 3,500 bees, after which Sites 2, 4, and 5 entered a linear growth phase. Site 1 increased slightly, then declined. Site 3 increased in an oscillating or

Table 1. Influences on modeled population growth of varying forager longevity and brood development timea

Peak population Peak population

Initial Foraging Established New Brood Established New colony time time

Climate size (d) Size Yo changeb Size 070 changeb (d) Size 070 changeb Size 070 changeb

Midwest 5,000 7 27,923 - 15,364 - 19 42,503 - 36,563 - 10 40,136 43.7 32,983 114.6 21 40,136 -5.6 32,983 -9.8 13 52,139 29.9 43,965 33.3 23 37,335 -7.0 21,668 -34.3

110 51,473 40.2 39,591 100.1 21 51,473 -3.1 39,591 -8.3 113 64,517 25.3 57,651 45.6 23 49,518 -3.8 35,040 -11.5

15,000 7 36,709 - 19,785 - 19 53,110 - 43,221 -

Southwest 5,000 7 21,594 - 14,429 - 19 37,154 - 36,761 - 10 35,917 66.3 32,456 124.9 21 35,917 -3.3 32,456 -11.7 13 45,945 27.9 45,780 41.1 23 34,687 -3.4 20,228 -37.7

10 46,448 44.1 41,146 109.5 21 46,448 -1.2 41,146 -3.8 13 57,915 24.7 52,272 27.0 23 45,224 -2.6 32,495 -21,O

15,000 7 32,222 - 19,640 - 19 47,030 - 42,777 -

“Egg-laying potential of 3,000 eggs/d; 365-d simulation; model estimates to nearest bee. Field estimates are likely to be

bPercent increase or decrease in population size; 7 d is reference value (0%) for foraging time variable; 19 d is reference within 10 to 15% of actual population size.

value (0%) for brood rime variable.


L a, n E 3 Z C 0 a, I

2000 1 I 0 7 14 21 28 35 42

Days

b

Fig. 3. (a) Best fit growth curves for bee colonies located along a heavy metal pollution gradient on Vashon Island, Washington. Site 1 had high exposures to As and Cd; Site 3 had moderate to high exposures to those metals; Sites 2, 4 and 5 had the lowest exposures. (b) Comparison of BEEPOP predictions of adult bee population size at time ( t ) to those estimated based on frame coverage or bee mass (final observation period) at Sites 2, 4 and 5 on Va- shon Island. Population estimates based on bee mass are more accurate than estimates from frame coverage, which varies with ambient temperature (i.e., bees form tighter clusters at low temperatures). Error bars are 95% confidence intervals (n = 14 for simulations, 24 for field measures).

stepwise manner and declined near the end of the experiment. Growth slowed during the fourth week at Sites 2 and 5, while Site 4 continued to increase in numbers of adult bees.

To demonstrate that BEEKILL-PC BEEPOP would approximate the “real world” results, simulations were conducted using weather data (Na- tional Climatic Data Center, Asheville, NC) from SeaTac International Airport, near the test tran- sect. For the simulations, each colony started with 4,000 to 4,700 workers: 30% foragers and 70% hive bees of randomly dispersed ages. Queen egg- laying potential was limited to 3,000 eggs/d, and there was no brood in the initial population. The ratio of brood to adults was set at 2: 1, based on ratios measured in the field. Brood nest space was

limited to 45% total comb area, again based on field collected data. The length of time adult workers remained in the hives prior to foraging was set at 30 to 32 d [17] and forager longevity at 10 d [ 121, the model’s default values for package colonies.

PC BEEPOP predicted (Fig. 3b) a final population size of 5,482 bees, compared to observed mean populations of 5,304 to 5,699 bees at the three lowest exposure sites. PC BEEPOP predicted 3,119 adult bees for the first observation period. Our field estimates for all five sites at that time gave an average population size of 3,315 bees; Site 4 was lowest with 2,786 bees. PC BEEPOP under- estimated the low point in the curve (2,436 vs. a field estimate of 3,628 bees). The linear portion of the predicted growth curve was steeper and peaked somewhat earlier than that observed in most of the minihives. Some of the minihives at the low-exposure sites peaked at the time predicted by the model and also declined slightly by the last observation period, as predicted by the model.

Additional simulations demonstrated that the model could reproduce the observed growth dynamics at Site 1, the highest exposure site. Our field data showed a significant (P < 0.01) reduction in adult bees compared to those at the low exposure sites and a small (about 15%), statistically in- significant reduction in brood. We simulated a 15% larval mortality, because the first larval in- stars immediately after hatching are thought to be the most vulnerable to toxic materials (H. Shima- nuki, personal communication). We then added mortalities ranging from 10 to 80% of hive bees and forager bees.

Increased brood mortality alone did not simulate Site 1, which had 40% fewer bees at the end of the field study than it had at the beginning. To du- plicate those results, the model had to kill 40 to 50% or more of all new adults over the six-week period plus 15% of the larvae. Alternatively, 2-d kills of more than 70% of the foragers were required to reduce the populations to the numbers measured.

DISCUSSION

The original BEEPOP allowed the user to ad- just the proportion of bees in each life stage, but not the default that assigned bees to the first day of age of each life stage. In PC BEEPOP, age distribution for each life stage is automatically assigned by a random number generator. The user can rerandomize or manually assign the number of bees for each day of age from 1 to 86.


Except for the fiIst few days of a simulation, the model was relatively insensitive to randomizing age structures, but was affected by changes in the life stage distribution:,. The predicted oscillatory increase of NEW colonies (started with all adult bees and no brood) appears to result from interactions of the 21- to 23-d brood cycles, periodic cohort losses of similar-aged bees and adu1t:brood ratios (i.e., the queen lays eggs and produces a brood faster than adults are replaced). This oscillation is consistent with observations of breaks in brood rearing and shifts in worker age distribution in colonies founded from packages [ 171.

Naumann and Winston [17] reported that the hive bee phase increases from about 24 to 32 d in package colonies. Our simulations indicated that extending the length of time bees spend as hive bees would permit larger brood numbers and dampen population oscillations.

Simulations of short-term mortality approximated an episodic hazard such as a chemical re- lease, a chemical spill or a pesticide application. For example, loss of 30% of the adults is equivalent to exposure to a highly toxic pesticide with an 8-h residue contact toxicity of 65 to 85% [18].

Acute losses of adult bees from mid-June through early August, when colonies forage and grow rapidly, usually reduced peak populations and altered growth curves the most, although recovery often was rapid. In nature, intensive foraging coincides with good weather and abundant flowers. Reducing forager populations at those times could significantly affect the colony’s yield of surplus honey (which is harvested) and other products. In addition, the colony may be unable to gather enough food to meet consumption needs. Development of a food gathering and consumption module is part of our ongoing research.

Exceptions exist to the generalization that short- term impacts have the. greatest effects on peak population size during periods of maximum colony growth. Our simulations suggest that there are critical periods, as short as a few days, in which a toxic event could decnmate the colony. Those periods often are characterized by low levels of adult bees (e.g., spring populations) or by low egg-laying rates (e.g., as occurs in autumn). A particularly critical period is the time just before emergence of the first brood, when adult populations reach a low point and are composed of mostly old bees. The model indicates that this situation is more pronounced in package than in established colonies. The model suggests that the queen may continue to lay eggs, but the colony has too few adults to prop-

erly care for them, and thus more brood dies. In the field, this could result in chilled or starved brood, shortages of pollen, expression of brood diseases and possibly cannibalism of brood.

Long-term, low-level mortalities reduced peak populations and also the size of the population entering winter. Short-term toxicity in late summer or early fall also reduced the size of overwintering populations. Bees cluster for warmth, and success- ful wintering requires sufficient food and young bees, along with protection from wind and mois- ture [l 11. Factors that reduce the size of a wintering population could increase the degree of attrition of the population and the probability of loss of the entire colony.

Losses of adult worker bees had a greater effect on population size than did losses of brood. Fer- tilized eggs become larvae in 3 d and adults in 21 to 23 d. Replacing an egg may take as few as 3 to 4 d, whereas replacing an adult takes several weeks. Furthermore, the egg-laying rate and the size of the adult population are interdependent, because adult bees are needed to rear brood. Hence, adult deaths from both long- and short-term toxicity affect all life stages.

In the field, hive entrance traps that collect dead bees and mark-recapture techniques can be used to monitor losses of adult bees. Estimates of brood developmental success and brood mortality can be made by estimating brood areas [19] and tracking marked cells through time [20]. Those approaches can be used to evaluate impacts on colony repro- duction. Losses of foragers should produce a shift- ing of labors among adults (e.g., hive bees foraging at an earlier age) and decrease production of honey and other hive resources. Losses of younger adults, such as housekeeping and nurse bees, could affect labors such as feeding brood and cleaning cells.

PC BEEPOP is a stage-based population model. Killing a percentage of the individuals within a life stage and then reducing recruitment to that stage may tend to exaggerate the effects of mortality, because individuals of all ages within a stage can be killed. Other means of killing bees are difficult in this type of program. Toxicity tests indicate that the age of adult honey bees affects tolerance to specific insecticides (e.g., newly emerged bees are more susceptible to DDT, dieldrin, and carbaryl, and older bees to malathion and methyl parathion [18,21,22]). Populous colonies tend to suffer greater losses than smaller colonies, because more of the adults are foragers and, as such, are exposed to the insecticide [ 181. Because PC BEEPOP differenti- ates between hive bees (younger adults) and forag-


ers (the oldest adults), PC BEEPOP allows the user some control of the age of affected adults.

Shortening forager life span or changing developmental rates is an alternative to reduced recruitment. Our simulations indicated that changing those parameters could have effects ranging from somewhat less than an equivalent chronic mortality to manyfold greater alterations in colony size (Table 1). New colonies exhibited the greatest changes, due to amplification of their oscillatory patterns of growth.

The model is relatively insensitive to short-term episodes of cold, rain, or wind. Small increases in numbers of adult bees often occurred because foragers could not fly. This increased their life span.

To validate the weather portion of the model, population response data from sites under different weather regimes over a period of several years would be needed. Aspects of habitat quality other than weather undoubtedly affect comparisons among sites. For example, forager life span affects peak population size and is a function of flight activity. Flight to distant flowers may reduce forager longevity because it increases the distance traveled each day [12]. Consequently, if comparisons are to be made among sites, all of the sites should have similar flower abundance and proximity to the hives. For sites with limited floral resources, sup- plemental feeding may be necessary to reduce this source of variability. Forager life span can be determined by marking bees of known age and intro- ducing them into colonies [23], whereas the size of the forager population can be estimated by using a nondestructive trap attached to the hive entrance

PC BEEPOP assumes that food is not limiting, if weather conditions permit foraging. This is an oversimplification. A colony will not grow and may even perish without sufficient food. Good weather does not guarantee that bees and the plants on which they depend will develop synchro- nously. We are developing a module that simulates the influence of the availability of nectar and pollen.

Our simulations confirmed that forager longevity and queen egg-laying potential greatly influenced colony population size [2]. At a hazardous waste site, the queen may be at greatest risk; her longevity also means a longer exposure to the toxin. We found that queens accumulate as many heavy metals as workers, or more (unpublished data). This exposure may reduce her egg-laying potential or even egg and spermatozoa viability. PC BEEPOP predicts that any disruption of the

~ 4 1 .

queen’s egg-laying would be deleterious to the colony.

PC BEEPOP’s ability to simulate accurately the dynamics of minicolonies used in the field test de- pended upon adjusting either forager life span or the number of days before bees switched from hive tasks to foraging. PC BEEPOP’s default value caused the model to remove too many adult foragers before the first brood cycle was completed.

Simulations that corrected PC BEEPOP’s un- derestimation of the population low tended to overestimate final population size for minicolonies. This was to be expected. Unless the user stipulates, PC BEEPOP does not take into account predation, accidents, hive space, and other factors that can reduce populations. We limited brood space to 45% of the total comb area, based on our field data. Harbo 1161 found that colonies with small combs produce less brood and fewer adults than do colonies with full-size combs. Based o n his equation for the effect of comb size on adult worker bee populations ( y = 16,794 + 9.68x, r = 0.66), the comb size of our minihives should decrease peak adult population size by l8%, which was approximated by the SPACE option of PC BEEPOP. Altering the ratio of brood to adults has effects similar to limiting hive space.

PC BEEPOP indicates that weather conditions among field sites would have had little or no effect on population size. Differences of a few days in forager life span could not have explained the stunted growth and eventual decline in population size at the highest exposure site (Site 1).

PC BEEPOP suggests that, to curtail population growth to the extent observed at this site, 15% of the larvae plus 70% (short term) of the foragers or 16 to 20% (continuous) of the new hive bees and the foragers have to die. The effect could also be induced by a 50% reduction in adult life span. Mortalities of that magnitude are consistent with measured tissue-residue concentrations of As in bees at the high exposure site. Bee tissue averaged more than 12 ppm As at Site 1 for all of the observation periods. This amount of As exceeds lethal levels for adult bees [25]. In addition, bees from that site had over 3.4 ppm Cd in their tissues.

Bees at the midisland site (Site 3) displayed fluc- tuating growth. Bees at that site averaged more than 7 ppm As and 2.8 ppm Cd. Those levels of As are reportedly somewhat toxic to adult bees [25]. PC BEEPOP predicts oscillations in the growth of package colonies, but not at the time intervals observed at Site 3. Changing parameters such as bee age distribution, forager longevity, and toxic


events altered the time intervals and either sup- pressed or amplified the oscillations.

Comparing model simulations to our field experiment is not intended as a validation of the model. We wanted to test PC BEEPOP’s potential in ecological assessments and the advisability of further testing and validation. Also, sensitivity analysis should determine which demographic parameters are critical to analyze risk.

BEEPOP was designed as a research model that could be modified as additional information becomes available. The combination of BEETOX- BEEKILL-PC BEEPOP provides a means of examining the influence of toxins on colony population dynamics, not just adult mortality. Our simulations demonstrated that P C BEEPOP is ca- pable of making plausible predictions and that it can help to isolate causes of colony growth or decline.

In any field toxicological assessment, even under relatively controlled experiments such as cage tests, there are uncertainties about the influence of other factors such as climate and site characteristics. Those factors are even more troublesome when the assessor attempts to establish a causal link between an adverse ecological effect and exposure to hazardous chemicals, especially if a closely matched control site cannot be found. Generally, relating observed responses to a physical or con- ceptual model and gathering a “preponderance of evidence” is the only way to demonstrate a causal link between ecological bee mortality and a toxicological hazard [26]. BEETOX-BEEKILL-PC BEE- POP provides a method to assess whether the observed alteration in bee colonies can be simply explained by a parameter such as weather or natural variation, or by some other factor such as a toxic episode.

CONCLUSIONS

P C BEEPOP is a personal computer version of the mainframe BEEPOP model. PC BEEPOP contains parameters that the user can vary that were not in the original BEEPOP program. They include an ability to enter the initial age structure of a colony, swarming, and differential mortality due to stress. Simulations of mortality due to pesticide exposure indicate that losses of adult bees affect colony population growth more than do losses from any other life stage. Comparisons of simulated and actual colony population growth indicate that P C BEEPOP can predict final population size within the 95% confidence interval of actual mean colony populations. The model can be used to pre-

dict colony population response to various weather and colony management scenarios. P C BEEPOP also can be used to estimate larval and adult worker bee mortality rates and resulting colony population dynamics at sites suspected of containing toxins or other pollutants.

Acknowledgement- We wish to thank C.A. Callahan, L.A. Kapustka and H. Kibby for their insightful and use- ful comments concerning assessment needs. Thanks also to V. Watson for modeling guidance and to S. Risland for her editorial assistance. This research was supported by Cooperative Research Agreement CR-814456 with the US. EPA’s Corvallis Environmental Research Labora- tory and by the University of Montana. The research de- scribed in this article does not necessarily reflect the views of the U.S. EPA; no official endorsement should be inferred.

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APPENDIX

BEETOX is a data base management system for honey bee toxicological data from controlled testing (e.g., laboratory bioassays and field cage studies) and in situ assessments of hazardous chemicals. Federal Insecticide, Fungicide and Rodenticide Act (FIFRA) guidelines con- ditionally require data on toxicity to non-target, benefi- cial insects for all pesticides used on food and non-food crops, on forests, and outdoors in domestic settings. Those tests include honey bee acute contact LD50, toxicity of residues on foliage, subacute feeding studies and field testing for pollinators. Other nations require similar tests, and several attempts have been made to harmo- nize test methods [27].

BEETOX was written in FORTRAN. It is an execut- able file for IBM-compatible PCs. BEETOX requires only DOS version 2.0 or higher to run. The chemical data base is contained in lookup tables. A program editor or word processor can easily add to the ASCII text tables.

The current data base contains information obtained from laboratory and field toxicology studies conducted since 1950 at the University of California at Riverside [28-321. It also contains field test data from the Washing-

Other data bases are being added. ton Agricultural Experiment Station at Prosser [33-351.

Many of the chemicals in the data base are organic

558 J. J. BROMENSHENK ET AL.

pesticides. Information is available for some inorganic chemicals, especially heavy metals. Most of the original pesticides (used before World War 11) were inorganic compounds of elements such as As, Cu, Pb, and S. In addition, there are also data from several decades of case history studies and litigation involving beekeepers and metal-emitting industriec;. BEETOX contains fewer references to industrial organics, although the data base does contain information about chemicals such as polychlorinated biphenyls (PCBs) and pentachlorophenol (PCP). Much of the data base consists of dose-response information based on toxicity to adult bees. Information about toxicity to brood is available for about 60 chemicals.

BEETOX lists more than 400 toxic chemicals, their formulas and synonyms, the Chemical Registry Number for each and the LD50 concentration in pg/bee. The tables also contain the correlation coefficient (if published), intercept, and slope value for the dose-probit mortality curve.

BEETOX uses the data base and probit analysis [36] to determine percent cumulative mortality for any dose of a toxin contained in the data base. BEETOX will also calculate the probable chemical exposure (pg/bee) from observed bee mortality. BEETOX assumes that the poi- son in question was completely responsible for the mortality. The program’s output also gives the type of test from which the data originated (e.g., contact toxicity, in- gestion, foliage residues), whether the test was field- or

laboratory-based, the age stage affected (adult or brood), relevant comments, and references.

Chemicals can be located in the data base by chemical formula, common name, or synonyms. Chemical abstract service (CAB) numbers help the user find more information about specific chemicals through other sub- structure and full-structure chemical search systems.

Help menus provide: (1) directions for program use, and (2) information about the data base, including a glos- sary, data sources, and employed equations. When ap- propriate, they provide guidance about limits of applicability of laboratory bioassays to predictions concerning toxicity under field conditions.

Linking BEETOX to PC BEEPOP interfaces the data base and our ecotoxicological model. Using P C BEE- P O P with BEETOX, a user can simulate population responses based on chemicals’ toxicities and other physical characteristics.

BEETOX alone provides information for many applications that include data management for pesticide reg- istration and regulatory decisions, ranking of chemicals according to risk to honey bees, better organization and compilation of honey bee toxicity data, and direct and immediate computer access to existing data. Potential users include personnel in government agencies, research- ers, beekeepers, and environmental groups. Uses range from providing basic toxicological information to in- service training programs.

Documents

PC BEEPOP, an ecotoxicological simulation model for honey bee populations