FISHERY BULLETIN: VOL. 87, NO. 4, 1989 



Reluctance to use TVOD to monitor the rela- 

 tive abundance of dolphins stem from concerns 

 that TVOD 1 ) seriously violate some of the fun- 

 damental assumptions of line transect analysis 

 (Polachek 1983), 2) are subject to serious but 

 unquantified and possibly inconsistent biases, 

 and 3) may be plagued with artifacts arising 

 from the data collection process. Artifacts in- 

 clude, for example, differences between RSOD 

 and TVOD in the sighting frequencies of various 

 dolphin species reported by observers on re- 

 search vessels compared to tuna vessels (Barlow 

 and Holt^), environmental factors affecting 

 sighting ability (e.g., sun glare, sea state, and 

 cloud cover; Holt and Cologne 1987), and shifting 

 areas of concentrated search effort (Buckland 

 and Anganuzzi 1988). However, problems of this 

 type are common to most commercial fisheries 

 data and analyses derived from them. It is im- 

 portant to determine whether, despite these dif- 

 ficulties, useful estimates can be derived from 

 such data sets. 



Toward this end, we have developed a 

 relatively simple model simulating the TVOD 

 collection process. Our purpose in developing 

 the model was twofold: 1) to test the effect of 

 suspected biasing factors on line transect 

 estimates of abundance and 2) to test new 

 methods of abundance estimation prior to con- 

 ducting expensive field tests. There are two 

 unique advantages of simulation modeling in 

 this context. First, we are simulating dolphin 

 abundances and vessel movements within the 

 model itself; therefore, we have available the 

 "truth" against which to compare our model- 

 generated estimates of abundance. Second, we 

 have the capability of investigating effects on 

 estimates that are due to combinations of bias- 

 ing factors which may not have occurred during 

 the years we happen to have been collecting 

 data, but which can be expected to occur. Bias- 

 ing factors include, for example, small-scale 

 nonrandomness in school and vessel movements 

 and spatial distributions, choice of data stratifi- 

 cation method, changes in fishing objectives, 

 practices, and areas of concentrated search, and 

 changes in sighting protocol and recording pro- 

 cedures. We chose to focus first on the effects of 

 nonrandomness and on the method of data 

 stratification because recently developed 



^Barlow, J., and R. S. Holt. 1986. Geographic distribu- 

 tions of species proportions for dolphins in the eastern tropi- 

 cal Pacific. Admin. Rep. No. LJ-84-27. Southwest Fish. 

 Cent., Natl. Mar. Fish. Serv., NOAA, LaJolla, CA. 



methods of line transect analysis to estimate 

 dolphin abundance from TVOD (Buckland and 

 Anganuzzi 1988) raised serious but unanswered 

 questions about the effects of these factors on 

 the abundance estimates derived. 



The philosophy behind building a relatively 

 simple model was that biases shown to be 

 troublesome and methods shown to be inade- 

 quate in a simple computer model are likely to be 

 even more troublesome and inadequate in the 

 real world. It is both more efficient and more 

 economical to investigate these biases and 

 methods first with a simple simulation model, 

 prior to developing expensive field experiments. 

 We have specifically applied the tenets of 

 Occam's Razor in developing this model, making 

 it as simple as possible while still incorporating 

 the major processes and features contributing to 

 the TVOD data collection process. In this study, 

 we focused only on estimating abundance of dol- 

 phin schools, leaving questions about abundance 

 of individual dolphins for a later day. We also 

 assumed that data were collected without arti- 

 facts, leaving also that problem for a later set of 

 simulations. Both of these omissions are ex- 

 amples of factors that probably have strong ef- 

 fects on analyses of TVOD, but which are at this 

 stage unnecessary refinements to the simulation 

 model. Such refinements could be added later if 

 no problems were identified during simulations 

 with the early, most simplified versions of the 

 model. 



This paper presents results of testing one 

 hypothesis about one of the most fundamental 

 factors suspected to affect seriously line transect 

 estimates of dolphin abundance derived from 

 TVOD. Specifically, we tested the effect of non- 

 random clustering by dolphin schools on abun- 

 dance estimates. As part of this analysis we 

 tested also the effects of three types of data 

 stratification prior to line transect estimation of 

 school abundance: 1) no stratification, 2) 

 stratification by raw encounter rate per 1° 

 square, and 3) stratification by smoothed en- 

 counter rate per 1° square, using the smoothing 

 and interpolation algorithm developed by the 

 Inter-American Tropical Tuna Commission for 

 deriving estimates of dolphin abundance from 

 line transect analysis of TVOD (Buckland and 

 Anganuzzi 1988). We were primarily interested 

 in the third type of stratification, because the 

 properties of the smoothing algorithm are poorly 

 understood. The other two stratifications were 

 conducted to provide a basis for comparison with 

 the smoothing procedure. 



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