1. Lecture 8 review Options for estimating population size –Direct census (visual, acoustic, etc.) –Density expansion (time, area) –Change in index methods (depletion, ratio) –C/U methods (Gulland’s old trick) –Pcap methods using marked animals –Bt/Bo methods using stock assessment models that estimate Bo as leading parameter Designing sampling programs for density –Define the sampling universe carefully –Use systematic sampling whenever possible

2. Lecture 9: Mark-recapture methods for abundance and survival Most important application and very broad need is to provide short-term estimate of exploitation rate U, to allow use of N=C/U population estimates, manage U change Mark-recapture data generally analyzed using binomial or Poisson likelihoods Multiple marking and recapture sessions over time can give estimates of survival and recruitment rate along with population size

3. Mark-recapture experiments Mark M animals, recover n total animals of which r are marked ones P cap estimate is then r/M, and total population estimate is N=n/P cap = nM/r, i.e. you assume that n is the proportion P cap of total N Critical rules for mark-recapture methods: 1.NEVER use same method for both marking and recapture (marking always changes behavior) 2.Try to insure same probability of capture and recapture for all individuals in N (spread marking and recapture effort out over population) 3.Watch out for tag loss/tag induced mortality especially with spagetti tags (use PIT or CWT when possible)

4. How uncertain is the estimate of Pcap (U) from simple experiments? Suppose M animals have been marked, and r of these have been recaptured Log Binomial probability for this outcome is lnL(r|M,Pcap)=r ln(Pcap) + (M-r) ln(1-Pcap) Evaluate uncertainty in Pcap estimate by either profiling likelihood or looking at frequency of Pcap estimates over many simulated experiments; get same answer, as in this example with M=50, r=10:

5. It takes really big increases in number of fish tagged to improve Pcap estimates The variance of the Pcap estimate is given by σ 2 pcap =(Pcap)(1-Pcap)/M, where M is number of fish marked. The standard deviation of Pcap estimates depends on Pcap and number marked:

6. Estimates of N=C/Pcap are quite uncertain for low M, eg 50 fish Generated using Excel’s data analysis option, random number generation, type binomial with p=0.2 and “number of trials”=50 This would be Lauretta’s luck, getting only 4 recaps when the average is 10 (0.2 x 50 marked fish)

7. How uncertain is the estimate of N from simple mark-recapture experiments? Suppose M animals have been marked, and r of these have been recaptured along with u unmarked animals Log Binomial likelihood for this outcome given any N is lnL(r,u|N)=r ln(Pmarked)+u ln(Punmarked)= r ln(M/N) + u ln((N-M)/N) Can also assume Poisson sampling of the two populations M and M-N –Pcap=(r+u)/N; predr=pcap*M, predu=pcap*(M-N) –lnL=-predr+r ln(predr) – predu + u ln(predu) Evaluate uncertainty in N estimate by profiling likelihood (show how lnL varies with N), as in this example with M=50, r=10, u=100:

8. Open population mark-recapture experiments (Jolly-Seber models) Mark M i animals at several occasions i, assuming number alive will decrease as M it =M i S t where S t is survival rate to the t th recapture occasion. Recover r it animals from marking occasion i at each later t. Estimate total marked animals at risk to capture at occasion i as TM i =Σ i-1 M i, to give Pcap i estimate Σ i-1 r it /TM i. Total population estimate N i at occasion i is then just N i =TN i /Pcap i, where TN i is total catch at i. Estimate recruitment as R i =N i -SN i-1 or other more elaborate assumption

9. Structure of Jolly-Seber experiments Make up a table to show mark cohorts and recapture pattern of these: Predict the number of captures for each table cell R ij =M i S (j-i) Pcap j (or N i,j-1 -r ij-1 )S if removed) Use Poisson approximation for lnL lnL=Σ ij [–R ij +r ij ln(R ij )] evaluated at conditional ml estimate of Pcap i =Σ i r ij /Σ i M i S (j-i) (only i’s present at sample time j)

10. Just remember these five steps Array your observed capture, recapture catches in any convenient form, C ij For each distinct tag (and untagged) group i of fish, predict the numbers N ij at risk to capture on occasions j, using survival equation (and recruitments for unmarked N’s) For each recapture occasion, calculate Pcap j as Pcap j =(total catch in j)/(total N at risk in j) For each capture,recapture observation, calculate the predicted number as =pcap j N ij Calculate likelihood of the data as Σ ij (- +C ij ln( ))

11. Don’t make stupid mistakes like this one Buzby and Deegan (2004 CJFAS 61:1954) analyzed PIT tag data from grayling in the Kuparuk River, AK; concluded there had been decrease in Pcap and increase in annual survival rate S over years, tried various models and presented lots of AIC values to justify the estimates below. In fact, (1) high Pcaps in early years are symptomatic of not covering the whole river in m-r efforts; (2) Pcap and S are partially confounded (can increase S and lower Pcap or vice versa, still fit the data).