Time Sequence of Events 



A flow chart of the computer program of 

 the deer herd is shown in Figure 3. For any 

 simulation model concerned with the flow of 

 variables over time, a unit of time must be 

 defined for purposes of calculation. The com- 

 puter moves in discrete steps through time, 

 and calculates the variables at each step. In 

 the deer herd model, the unit of time is one 

 month. For each month of a computer run, 

 the relevant calculations are made, and the 

 status of the system at the end of that month 

 is generated. The status of the system is an 

 array of rates and levels for all variables in 

 the system. The time counter is advanced one 

 unit (one month) and the appropriate calcu- 

 lations for that month are made. Calculations 

 can be made conditional upon any event or 

 series of events in the past, but not upon 

 future events, because they have not occurred. 



Starting with the opening inventory shown 

 at the top of Figure 3, the computer program 

 selects a forage factor for the year as of Novem- 

 ber 1, and computes natural losses as a con- 

 sequence of the forage factor and deer density. 

 Figure 2 shows the array of interactions which 

 are summarized in the mortality rate-density 

 functions. The mortality rate in each age and 

 sex class is described as an exponentially in- 

 creasing function of density. Hunting losses 

 are then computed in accordance with the 

 hunting strategy specified for the simulation 

 run, and the closing inventory by age and sex 

 is calculated. Loss totals are then accumulated, 

 and can be included in the output as desired 

 by the analyst. Each month, the above sequence 

 of events is carried out. 



After accumulating losses in May, the num- 

 ber of new fawns to be introduced into the 

 herd is computed. The birth rate in each age 

 class of does is described as a decreasing func- 

 tion of the exponential average density. The 

 age categories are then advanced one year. 

 Bucks and does in their sixteenth year are 

 removed from the system — represented in 

 Figure 2 by the sink. Fawns born 12 months 

 previously are separated into bucks and does, 

 and redefined as deer in their second year. 



Two accounting years are defined in the 

 computer program. The first is from November 

 1 to October 31. November 1 is the time when 



managers are best able to make population 

 counts indicative of the age and sex composi- 

 tion of the herd. The second accounting year 

 used in the model, July 1 to June 30, facilitates 

 the summarization of the hunting results for 

 each year. Selected parameters are printed 

 at the end of each accounting year. After all 

 the October operations are performed, the 

 year counter is advanced and the simulation 

 proceeds until the specified number of years 

 has been executed. At the end of each run, sum- 

 mary statistics are printed. 



Input Data 



The model is intended to simulate the Men- 

 docino County deer herd, but the primary 

 data source was the University of California 

 Field Station at Hopland, where the deer 

 population has been under continuous and in- 

 tensive study since 1951. The investigators 

 at Hopland compiled these data and integrated 

 them with the California Fish and Game De- 

 partment data for the remainder of the county. 



Data input for each run is separate from 

 the computer program. This permits changes 

 in the data assumptions to be made without 

 altering the computer program. The program 

 is designed to be applicable, with minor modi- 

 fication, to other big game populations. 



The data block for each run includes constants 

 to initialize the run, such as the opening in- 

 ventory, the area of land available to the herd, 

 and the length of the run. Other data used in 

 each year include birth rate and natural mor- 

 tality functions, hunting loss percentages, and 

 the distribution of forage factors. 



HUNTING STRATEGY RESULTS 



While an infinite variety of hunting strate- 

 gies can be tested in this moL.el, the options 

 of the wildlife manager are limited because 

 certain hunting strategies that are biologically 

 feasible may be socially or politically unaccept- 

 able. In addition, hunters can usually dis- 

 tinguish only a few age and sex classes in the 

 field. Limited hunter access to extensive 

 areas of private forest and range lands pre- 

 cludes the achievement of uniform hunting 

 pressure over the entire county. 



126 



