Table 10 — Weibull distribution functions for determining the number of trees 

 per stocked plot 



TPSP = 



Bl-\n{^-x)y'^+^ 



wner c 





TPSP = 



number of trees per stocked plot 



X = 



a uniform pseudo-random number in the interval [0,1] 



B = 



EXP[1 .79862 + 0.64299*COS(ASP)*SLO 





- 0.34931 *SIN(ASP)*SLO - 2.18751*810 - 0.0381 5*ELEV 





+ U.^/yo/ bUHbo 1 + 0.1oo/4 bUbWAr 





+ 0.07241 if Abies grandis series 





+ 0.85008 if ThujalTsuga series 





+ 0.64994 if Abies lasiocarpa series] 



C = 



EXP[- 0.33367 - 0.00751 *ELEV + 0.071 64*SQREGT 





+ 0.06127*SQBWAF] 



Table 11 — Equations for determining the number of species on stocked plots. The form of the equation is P= (1+e"*^''^'')"\ 

 The equation for one species is conditional on there being at least two trees on the plot, while other equations are 

 conditional on the number of trees being equal to or greater than the number of species being predicted 



Variablp 



1 cnppiAC 











6 enPClP^ 



/ (X) 



\V>I 



\Pi 



VM/ 



(B) 





(B) 



VP/ 



1 Constant 



1 .39959 



-0.44188 



-2.05274 



-6.24551 



-2.04323 



-5.93804 



2 COS(ASP)*SLO 







.57776 



.07506 



-1.27740 











3 SIN(ASP)*SLO 







.29407 



-.47116 



-.32990 











4SL0 







-.12877 



-.84038 



.42524 











5 REGT 



-.02217 



.01657 



.00475 



.00137 











6BWAF 



-.03940 



-.00025 



.07635 



.07188 











7 ELEV 



.02139 



-.01104 



-.01859 







-.05291 







8 BAA 



.00216 























9 PLANT 



-.39803 







.28128 



.61675 











10 TPSP 







-.01013 



.05466 



.05678 



.02188 



.02007 



1 1 TPSP^ 











-.00047 



-.00024 











12ln(TPSP) 



-1.01790 























Overstory climax 















13 ^PSME 



























14 ABGR 



-.69564 



.43695 



.67734 



2.30190 











15 THPL 



-.77642 



.42663 



.90042 



2.60261 











16 TSHE 



-1.22760 



.36357 



1.29021 



3.08950 











17 ABLA 



-1.05898 



.72147 



.90065 



2.15632 











Chi-square 



14.94 



7.01 



20.35 



13.82 



13.29 



1.00 



Error mean square 



0.9900 



1.0028 



0.9975 



0.9223 



0.9402 



0.8091 



^Percent occurrence 



40.34 



39.24 



19.79 



6.87 



2.35 



0.41 



No. plots 



4,370 



4,370 



3,269 



2,678 



2,213 



1,936 



Optimum aspect 



NS 



27° 



279° 



194° 



NS 



NS 



Amplitude 





0.10 



0.07 



0.20 







'Represented as part of the constant term to avoid a singular matrix. 



^Percent occurrence for dichotomous dependent variables is the percentage of plots on which the event occurred. For example, 1 ,763 of 

 4,370 stocked plots had only one species; therefore, percent occurrence is 40.34. 



46 



