This exercise is designed to show how to run programs PRESENCE to compute species presence, detectability, extinction and colonization estimates from 'presence-absence' data which includes covariates.
Input data consists of 'detection-histories' of individual species at 'stops' along bbs survey routes. Site covariates, which place the species in groups according to migration properties have also been included.
You can view the data by:
You're now presented with a 'Results Browser' window where a summary of each model will be saved. To run our first model:
After the analysis is complete, click 'yes' to append the output to the results browser.
Next, let's run a model where occupancy (psi) is a different for each covariate group (psi(short migrators) not= psi(residents) not= psi(neotropical migrants)). We'll name it psi(migr),gam(.),eps(.),p(.). Start by adding 2 columns to the Occupancy design matrix.
Run this model.
Look at the output for this model.
PRESENCE - Presence/Absence-Site Occupancy data analysis Mon Mar 24 15:06:41 2008, Version 2.080310 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ==>i=C:\x\workshops\france2008\bbs_example\bbsmd.pao ==>l=C:\x\workshops\france2008\bbs_example\bbsmd.pa2.out ==>name=psi(migr),gamma(),eps(),p() ==>model=2000 ==>j=C:\x\workshops\france2008\bbs_example\bbsmd.dm ==>lmt=200 model=2000 N,T-->85,25 modtype-->2 Multi-Season data Model selected Data checksum = 14209 NSi-->3 site_covname[0]=SHORT site_covname[1]=RESIDENT site_covname[2]=NEOTROP NSa-->0 Primary periods=5 Secondary periods: 5 5 5 5 5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - BBS data from WI, 1970-1990 (every 5th year) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - modtype=2 N=85 T=25 Groups=1 bootstraps=0 -->0 5 Primary periods Secondary periods: 5 5 5 5 5
Model(1):psi(migr),gamma(),eps(),p() psi covariates: Matrix 1: rows=2, cols=4 -,a1,a2,a3, psi1 SHORT RESIDENT NEOTROP**** logit(psi) = SHORT*β1 + RESIDENT*β2 + NEOTROP*β3
======================== gam covariates: Matrix 2: rows=5, cols=2 -,b1, gam1 1 gam2 1 gam3 1 gam4 1 ======================== eps covariates: Matrix 3: rows=5, cols=2 -,c1, eps1 1 eps2 1 eps3 1 eps4 1 ======================== detection covariates: Matrix 4: rows=26, cols=2 -,d1, P[1-1] 1 P[1-2] 1 P[1-3] 1 P[1-4] 1 P[1-5] 1 P[2-1] 1 P[2-2] 1 P[2-3] 1 P[2-4] 1 P[2-5] 1 P[3-1] 1 P[3-2] 1 P[3-3] 1 P[3-4] 1 P[3-5] 1 P[4-1] 1 P[4-2] 1 P[4-3] 1 P[4-4] 1 P[4-5] 1 P[5-1] 1 P[5-2] 1 P[5-3] 1 P[5-4] 1 P[5-5] 1 ========================
Open Population Model: Number of sites = 85 Total number of sampling occasions = 25 Number of primary sampling periods = 5 Number of missing observations = 0 Number of parameters = 6 -2log(likelihood) = 2272.742322 AIC = 2284.742322 Model has been fit using the logistic link. Untransformed Estimates of coefficients for covariates (Beta's) ============================================================================== estimate std.error A1 :occupancy psi1SHORT 0.575387 (0.370320) A2 :occupancy psi1RESIDENT 1.216880 (0.669897) A3 :occupancy psi1NEOTROP 1.828188 (0.471918) B1 :colonization gam1 -1.626977 (0.284866) C1 :local extinction eps1 -1.858940 (0.215355) D1 :detection P[1-1] 0.209428 (0.057745) Variance-Covariance Matrix of Untransformed estimates: A1 A2 A3 B1 C1 D1 A1 0.137137 0.001030 0.000664 -0.003782 -0.001047 -0.000635 A2 0.001030 0.448762 0.000040 -0.000340 0.000436 -0.000119 A3 0.000664 0.000040 0.222707 -0.003727 -0.000712 -0.000729 B1 -0.003782 -0.000340 -0.003727 0.081149 0.010409 0.002177 C1 -0.001047 0.000436 -0.000712 0.010409 0.046378 0.001854 D1 -0.000635 -0.000119 -0.000729 0.002177 0.001854 0.003335 ------------------------------
Individual Site estimates of Psi: Site Survey Psi Std.err 95% conf. interval 1 site_1 1 survey_1: 0.8615 0.0563 0.7116 - 0.9401 2 site_2 1 survey_1: 0.6400 0.0853 0.4625 - 0.7860 3 site_3 1 survey_1: 0.6400 0.0853 0.4625 - 0.7860 4 site_4 1 survey_1: 0.7715 0.1181 0.4760 - 0.9262 5 site_5 1 survey_1: 0.6400 0.0853 0.4625 - 0.7860 6 site_6 1 survey_1: 0.8615 0.0563 0.7116 - 0.9401 : : Distribution of Psi's: 0.00 0: 0.03 0: 0.05 0: 0.07 0: 0.10 0: 0.13 0: 0.15 0: 0.17 0: 0.20 0: 0.23 0: 0.25 0: 0.28 0: 0.30 0: 0.33 0: 0.35 0: 0.38 0: 0.40 0: 0.42 0: 0.45 0: 0.47 0: 0.50 0: 0.53 0: 0.55 0: 0.57 0: 0.60 0: 0.63 32:**************************************** 0.65 0: 0.68 0: 0.70 0: 0.72 0: 0.75 13:**************** 0.78 0: 0.80 0: 0.82 0: 0.85 40:************************************************** 0.88 0: 0.90 0: 0.93 0: 0.95 0: 0.97 0: 1.00 0: Individual Site estimates of Gamma: Site Survey Gamma Std.err 95% conf. interval 1 site_1 1 survey_1: 0.1642 0.0391 0.1011 - 0.2557 Individual Site estimates of Eps: Site Survey Eps Std.err 95% conf. interval 1 site_1 1 survey_1: 0.1348 0.0251 0.0927 - 0.1920 Individual Site estimates of p: Site Survey p Std.err 95% conf. interval 1 site_1 1 survey_1: 0.5522 0.0143 0.5240 - 0.5800 ================================================================================ DERIVED parameters - psi2,psi3,psi4,...These parameters can be computed from the others (psi,gam,eps)
Site psi(t) Std.err 95% conf. interval 1 site_1 psi( 2): 0.7681 0.0449 0.6801 - 0.8561 1 site_1 psi( 3): 0.7026 0.0443 0.6158 - 0.7895 1 site_1 psi( 4): 0.6567 0.0474 0.5638 - 0.7497 1 site_1 psi( 5): 0.6246 0.0511 0.5243 - 0.7248 2 site_2 psi( 2): 0.6128 0.0626 0.4901 - 0.7356 : : 85 site_85 psi( 5): 0.5711 0.0540 0.4653 - 0.6769 DERIVED parameters - lam2,lam3,lam4,... Site lam(t) Std.err 95% conf. interval 1 site_1 lam( 2): 0.8916 0.0278 0.8371 - 0.9460 1 site_1 lam( 3): 0.9148 0.0230 0.8698 - 0.9597 : : 81 site_81 lam( 4): 0.9347 0.0192 0.8970 - 0.9724 81 site_81 lam( 5): 0.9510 0.0161 0.9194 - 0.9826Next, let's run a model where extinction (eps) and colonization (gam) are also different for each covariate group. We'll name it psi(migr),gam(migr),eps(migr),p(.). Start by retrieving the last model run.
Run this model.