NOAA Technical Memorandum NMFS-PIFSC-4

March 2005

Evaluation of Time-area Closures to Reduce 

Incidental Sea Turtle Take in the Hawaii-based 

Longline Fishery: Generalized Additive Model (GAM)

Development and Retrospective Examination 

Donald R. Kobayashi 

 and 

Jeffrey J. Polovina 

Pacific Islands Fisheries Science Center 

National Marine Fisheries Service 

National Oceanic and Atmospheric Administration 

U.S. Department of Commerce 

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___________________________________________________________ 
 Pacific Islands Fisheries Science Center 

 National Marine Fisheries Service 

 National Oceanic and Atmospheric Administration 

 U.S. Department of Commerce

Evaluation of Time-area Closures to Reduce 

 Incidental Sea Turtle Take in the Hawaii-based 

Longline Fishery: Generalized Additive Model (GAM)

Development and Retrospective Examination 

Donald R. Kobayashi 

and 

Jeffrey J. Polovina 

Pacific Islands Fisheries Science Center 

2570 Dole Street 

Honolulu, Hawaii 96822-2396 

NOAA Technical Memorandum NMFS-PIFSC-4 

March 2005

 iii 

ABSTRACT 

 Generalized additive models (GAMs) of sea turtle take in the Hawaii-based 

longline fishery were developed at the National Marine Fisheries Service (NMFS), Pacific 

Islands Fishery Science Center (PIFSC) to identify time-area closures that would 

effectively reduce interactions with sea turtles while minimizing hardship to longline 

fishermen. Detailed observations gathered by NMFS Southwest Region (NMFS-SWR) and 

NMFS Pacific Islands Region (NMFS-PIR) observers assigned to the longline fleet were 

used to develop the GAMS. The GAMs were then used in predictive mode to estimate 

turtle take over the entire longline fleet using federally mandated logbook data. Highresolution environmental data were merged with the fishery data in an attempt to find 

useful covariates of turtle take. Computer simulation was used to assess the impact of 

seasonal (monthly resolution) closures or spatial (whole degrees of latitude/longitude 

resolution) closures over a systematic grid of 361,194 possible closure scenarios. 

Leatherback turtles were of primary concern because of their endangered status. 

Immediate impacts to the fishery were measured by predicting the fraction of the fleet 

displaced spatially or temporally by the proposed management action. Long-term and 

financial impacts were also estimated using models of fishing effort reallocation and 

predicted catch rates of the displaced fishing effort coupled with market revenue data. 

Variability was addressed by the randomization procedure called bootstrapping. “Efficient 

frontier” analysis was used to visually determine the efficacy of proposed management 

scenarios. This approach is used primarily in Modern Portfolio Theory but has wide 

applicability for the identification of optimal solutions in a complex setting. Due to the 

widespread patterns of leatherback turtle take (primarily in space, but also in time), it was 

difficult to define an optimal management scenario that could substantially reduce 

leatherback takes with a minimal impact to the fishery. However, the Emergency Closure 

(November 1999) of the fishery was shown to be quite distant from the efficient frontier. 

The GAM results were evaluated by using a substantially larger (4.5X) database of more 

recent observer data, facilitated by the increased rate of observer coverage of the fleet as 

mandated by the federal court. These findings indicate that the initial time/area closure 

analysis was robust with respect to general patterns of turtle take in time and space for 

loggerheads and leatherbacks, the two species primarily encountered by the fishing fleet. 

INTRODUCTION 

 Longline fisheries and their interaction with protected species have recently 

become a forefront issue in fisheries management and policymaking. A variety of seabirds, 

sea turtles, and marine mammals are protected by law (e.g., U. S. Endangered Species Act 

and Marine Mammal Protection Act), yet often undergo injurous or fatal interactions with 

longline fishing gear (e.g., Bearzi, 2002; Carreras et al., 2004; Kotas et al., 2004; 

Weimerskirch et al., 1997; Witzell, 1999); and the overall ecological impact is asserted to 

be substantial (Crowder and Myers, 2001). It should be noted, however, that reducing 

longline bycatch may be much less important than beach protection for some sea turtles 

(Pritchard, 1996). 

 The Hawaii based longline fishery is a year-round, limited-entry, high-seas fishery 

targeting billfishes and tunas in the central Pacific Ocean (Ito and Machado, 2001). Most 

fishing activity takes place in the region bounded by latitude 0 to 45°N, longitude 180° to 

140°W (Fig. 1). Over the pre-litigation time period 1994-1999 an average of 114 active 

vessels made 1,153 fishing trips and 11,888 longline sets in this fishery annually (Table 1). 

Observer coverage over this time period averaged less than 5% of the fleet. Sea turtle 

interactions in the Hawaii-based longline fishery primarily involve four species with wide 

geographic ranges throughout the eastern and Indo-West Pacific Ocean: loggerhead 

(Caretta caretta), leatherback (Dermochelys coriacea), olive ridley (Lepidochelys 

olivacea), and green (Chelonia mydas). Loggerheads are the most commonly encountered 

species, with an average annual fleet-wide take of 418 individuals over the same prelitigation time period 1994-1999 (Table 2). Leatherbacks, olive ridleys, and greens 

followed with averages of 112, 146, and 40 individuals taken per year, respectively. It 

should be pointed out that not all turtles that are “taken” are dead or will necessarily die 

later. The kill rates (kills per take) presently used depend on the severity of the hooking or 

entanglement, the type of gear configuration used, and also vary by species. Fleet-wide 

average annual kills over the same time period were estimated to be 168, 37, 73, and 18, 

respectively (Table 2). Historically, these takes have been well documented (e.g., Kleiber, 

1998; McCracken, 2000; Nitta and Henderson, 1993). 

 Mortality estimates have varied, and the final estimates given in Table 2 follow an 

official NMFS policy (NMFS, 2001). Efforts are underway to better estimate (Epperly and 

Boggs, 2004) and understand mortality (e.g., Work and Balazs, 2002, Hays et al., 2003) as 

well as to develop mitigation techniques to reduce mortality (e.g., Bolten and Bjorndal, 

2002; Polovina et al., 2003; Boggs, 2004; Watson et al., in press). 

 Sea turtles appear to have well-defined pelagic habitat requirements based on a 

composite of surveys, satellite tagging, and remotely sensed data (Coles and Musick, 2000; 

Polovina et al. 2000; 2004); and their incidental take in pelagic longline fisheries follows 

distinct spatial and temporal patterns (Witzell, 1999). These patterns are not particularly 

surprising considering that many sea turtle species have predictable transoceanic migration 

routes (e.g., Nichols et al., 2000) or predictable oceanographic regions of relatively higher 
 2

residence times (Polovina, personal communication). The intersection of these migratory 

pathways or high-residence areas with fishery activities results in a mutually deleterious 

situation for sea turtles and fishermen. One logical approach to remedy this undesirable 

overlap in time and space is to impose a type of fishery management tool termed a timearea closure, which restricts fishing activity to a designated geographic fishing area and a 

designated fishing season. Despite the impression by some that time-area closures are a 

“blunt tool” in fisheries management (e.g., Curtis and Hicks, 2000), such closures have 

been shown to have great potential in reducing unwanted bycatch while minimizing 

changes to target species catch (e.g., Goodyear, 1999), and have been explored as possible 

tools in reducing sea turtle take in the Hawaii-based longline fishery (Kobayashi and 

Polovina, 2001; Chakravorty and Nemoto, 2001). Obviously, if the species of interest has a 

predictable distribution in time and space, this would facilitate the designing of an effective 

time-area closure. 

 Generalized additive models (henceforth GAMs) are a relatively new analytical 

technique (Hastie and Tibshirani, 1990) and have been widely utilized in quantitative 

fishery applications including topics as diverse as size at maturity (Watters and Hobday, 

1998), habitat use (Knapp and Preisler, 1999; Stoner et al., 2001), stock-recruitment 

(Jacobson and MacCall, 1994), and survey/assessment (Swartzman et al., 1992; Borchers 

et al., 1997; Bigelow et al., 1999; Forney, 2000; Walsh and Kleiber, 2001; Walsh et al., 

2002). GAMs are useful when the predictor variables have nonlinear effects upon the 

response variable. For example, the abundance of a particular species may increase as a 

function of latitude or longitude, and then decline, as a characterization of a preferred 

habitat. GAMs can quantify these types of distributional patterns using flexible 

nonparametric smoother functions. 

 

 This report documents a series of steps taken at the National Marine Fisheries 

Service, Pacific Islands Fisheries Science Center in response to the Order issued by Chief 

U.S. District Court Judge David Alan Ezra, District of Hawaii, in the case of CMC et al. 

versus NMFS et al.; CIVIL NO. 99-00152; dated November 23, 1999, to complete an 

analysis of the temporal and spatial distribution of interactions between Hawaii-based 

longline vessels and sea turtles to determine time and area closures that would provide the 

greatest benefit to the turtles. Firstly, GAMs will be developed for each species to enable 

prediction of sea turtle take fleet-wide on a set-by-set basis. Secondly, a large number of 

potential time-area closures will be evaluated. Thirdly, the Emergency Closure ordered by 

the federal court in November 1999 will be compared to these findings. Lastly, the GAMs 

will be evaluated in retrospective fashion using a much larger database (~4.5X increase, 

12,688 sets vs. the initial 2,812 sets) of observed sea turtle takes. Leatherbacks are 

presently considered to be the most threatened of all of these species (e.g., Spotila et al., 

1996); therefore, closures most beneficial to leatherbacks will be the focus of this report. 

Since there is much controversy and ongoing research involving post-hooking turtle 

mortality, all impacts presented in this report are in the form of a scale-free percent change 

to turtle take, which could apply equally to turtle kills as well, once the latter are better 

estimated. The complete findings are presented elsewhere with a lengthy appendix of 
3

candidate time-area closures (Kobayashi and Polovina, 2001), and is also available online 

at http://swr.nmfs.noaa.gov/pir/pfseis/AppendixH.pdf. 

METHODS 

Previous analyses of sea turtle take in the Hawaii-based longline fishery have 

primarily focused on overall annual take numbers (e.g., Kleiber, 1998, 1999; McCracken, 

2000), with less emphasis on take by area or month. These latter types of data are essential, 

however, toward constructing effective time-area closures. The first objective of this report 

was to create a database of turtle take structured over time and area. This was 

accomplished by applying predictive GAMs to the attributes of each set of longline gear. 

These attributes include variables reported in the mandated federal logbook such as 

latitude, longitude, trip type (a 3-value index expressing the fish species targeting practice 

of that particular fishing trip: swordfish, tuna, or mixed), month, and year. Other variables 

such as moon phase and satellite-measured sea surface temperature (weekly 0.1° lat./lon. 

resolution multichannel sea surface temperature, MCSST, using NOAA AVHRR polarorbiting satellites, data available from the University of Miami) were merged with the 

logbook data independently for this analysis, using exact location and date to determine the 

corresponding values. The GAMs for predicting sea turtle take were constructed from 

detailed observations gathered by NMFS-SWR and NMFS-PIR observers, who monitored 

approximately 3%-5% of the total longline fleet activity during the pre-litigation years 

1994-1999 (Table 1). Observers are required to tally all turtle takes, and other ancillary 

data. Modeling fleet-wide turtle take from this small subset of the data is preferable to 

using logbook data verbatim for these controversial interactions with protected species. 

Earlier work has shown that logbook-derived estimates of turtle take account for only 

about 9% of the total take; i.e., there are about 11 times more turtles being taken than 

logbook data alone would indicate (Dinardo, 1993). 

GAMs differ from more conventional models in that they can easily incorporate 

complex nonlinear effects from multiple sources. Other commonly used models such as 

generalized linear models (GLM), analysis of variance (ANOVA), and multiple 

regressions all assume a form of linearity with the predictor variables. However if we are 

attempting to model the per-set take of loggerhead turtles against SST, for example, it 

might a priori be expected that there is an optimal temperature when turtle take is high and 

varying degrees of reduced take to either side of this optimum turtle habitat (Polovina et 

al., 2000). Conventional linear approaches would fail to capture this effect, or could 

produce misleading or nonsensical predictions because of a forced linearization of the 

underlying process. Linearity remains a special case of the GAM and can be 

accommodated if the data suggest such an effect. When dealing with a suite of unknown 

effects, a conservative or precautionary approach should include models that can handle 

complex nonlinear effects as well as the simpler linear effects. The nonlinear effects in a 

GAM are expressed as a smoother function of each variable, whose sum effect (hence 

additive) results in the predicted value of interest, in this case species-specific per-set sea 

turtle take. There are several choices for smoother function specification in a GAM; in our 
4

application we chose to use smoothing splines since these generally perform better with 

regard to the bias-variance tradeoff than lowess or kernel smoothers (Trevor Hastie, 

personal communication). 

Turtle-take GAMs were constructed using the software package S-Plus (v. 3.4) 

running under IRIX 6.5.4 on an SGI Challenge L workstation. Observer data (n=2,812 

sets) and logbook data from un-observed trips (n=55,785 sets) from the years 1994-1998 

were initially examined. A set of variables common to both the observer and logbook 

databases was evaluated in the GAMs: latitude, longitude, trip type, month, and year, as 

well as the added variables moon phase and satellite- measured SST. Stepwise procedures 

were used to identify variables with a statistically significant contribution toward 

predicting turtle take. The stepwise procedure starts out with a fully saturated model with 

all variables specified with smoother functions, then the model is simplified by eliminating 

variables or using linear functions instead of nonlinear smoother functions. The rearward 

stepwise approach is favored in this type of statistical model (Trevor Hastie, personal 

communication). The statistical criterion used for the automated acceptance or rejection of 

terms in the GAM is called the Akaike Information Criterion (AIC, see Akaike, 1974), 

which is a goodness of fit index penalized by the number of parameters in the model. 

Individual GAMs were run for each turtle species: loggerhead, leatherback, olive ridley, 

and green. Degrees of freedom in each smoother function were constrained (df=2) to 

eliminate extraneous curvature. The individual smoother functions for each of the final 

models are shown graphically in Figures 2-5. The final loggerhead GAM included 

smoothed nonlinear effects from latitude, longitude, moon phase, and SST, and categorical 

effects from each month and each year. The final leatherback GAM included smoothed 

nonlinear effects from latitude and moon phase, a linear effect from longitude, and 

categorical effects from each month. The final olive ridley GAM included a smoothed 

nonlinear effect from moon phase and a linear effect from SST. The final green GAM 

included a linear effect from longitude and a smoothed nonlinear effect from moon phase. 

These GAMs were used to make per-set take predictions across the entire logbook 

database using the selected variables for each turtle species. Exploratory plots 

summarizing turtle take by latitude, longitude, and month were created from the final turtle 

take database. Each GAM was bootstrapped 100 times using random permutations of the 

observer database, and the 95% variability bands were estimated from the distributions of 

the smoother functions (Efron & Gong, 1983). These nonparametric or empirical 

variability bands were constructed by sorting the binned smoother function values from 

low to high and using the 2.5%th and the 97.5%th values to identify the medial 95% of the 

distribution. 

 

The logbook database of turtle take was then examined in a series of computer 

simulations mimicking the effects of various protective management scenarios. 

Management scenarios were restricted to seasonal and spatial closures of the longline 

fishing grounds. Seasonal closures consisted of single month and adjacent multimonth 

closures spanning all possible combinations from 1 to 11 months in duration. Spatial 

closures consisted of latitudinal closures (i.e., “no fishing north of...”), longitudinal 

closures (i.e., “no fishing east of...”), and box closures, which combined the characteristics 
5

of a latitudinal closure and a longitudinal closure. These constraints on fishing effort were 

chosen because of the predominantly northward and westward distributions of leatherback 

turtle take and the orientation of the fishing grounds with respect to the Hawaiian island 

chain; hence, spatial closures were examined at the resolution of whole degrees with “north 

of” values ranging from latitude 20°N to 40°N and “east of” values ranging from longitude 

174° W to 145°W. Seasonal and spatial closures were combined in two possible ways 

with the first being a “separated” mode where the seasonal closure impacts all areas for the 

duration of the closed season, while the spatial closure is in effect for all time. The second 

type of seasonal and spatial closure combination is a “merged” mode, where the seasonal 

closure applies only to the spatial closure region. Similarly, the spatial closure is only for 

the duration of the seasonal closure. In any given management action simulation, fishing 

effort, fish catch, fish catch revenue, and turtle take of all species were tabulated under two 

modes of effort reallocation. In the “static” mode, fishing activity was assumed to not 

adjust after any management action, and any fishing effort lost because of spatial and/or 

seasonal closures was simply ignored. In the “dynamic” mode, the fishery was assumed to 

respond to the closures in a predictable manner. For spatial closures, it was assumed that 

complete spatial reallocation of lost fishing activity would occur, and this was modeled 

using monthly trip type-based expansions of open-area fishing activity. For seasonal 

closures, it was assumed that a maximum of one month’s fishing activity could be 

reallocated symmetrically to adjacent months bounding the seasonal closure; operationally 

this was approximated by allocating each lost set with a multiplier of 0.5/(number of 

closed months) to each bounding month. For this report, all years of data were combined 

to provide an average historical effect of a given management scenario. It should be noted 

that reallocation is based upon existing fishing patterns and this leads to several important 

points: 1) reallocation of fishing effort could possibly not occur, if there were no entries in 

the appropriate month-trip type-area strata, 2) unfished month-trip type-area strata would 

remain unfished; i.e., we do not account for possible expansion of the fishing grounds, and 

3) management mechanisms currently in place and reflected in the data are accounted for 

in the reallocation; i.e., protective influences of the existing 50 nmi longline closures 

around the Northwestern Hawaiian Islands are retained, and all predicted changes from 

status quo are considered as supplemental to existing effects. 

Fish catch revenue impacts of each closure scenario were calculated based upon the 

values of individual fish kept or lost due to the closure. These data were made available 

from economic analyses of the pre-litigation longline fishery (Sam Pooley, personal 

communication). Ex-vessel prices ($/pound) and values ($/fish) were calculated for all 

major species by month and trip type (broadbill, mixed, tuna) for the 1998 fishing year by 

merging the NMFS sample of wholesale market prices with trips identified in the NMFS 

logbook reports for 1998. These values were applied to estimated catches in each timearea stratum to estimate ex-vessel revenues. The NMFS wholesale price sample was 

roughly 30%-35% of all longline transactions in 1998. Previous analysis has shown 

consistency with the Hawaii Division of Aquatic Resources longline price reports for 

recent years. Where no data were available for a species-month-trip type stratum, 

extrapolations for that month and species were used, weighted by annual average 

differences between trip types. 
6

After accounting for all possible combinations of seasonal and spatial closures 

under separated, merged, static, and dynamic modes, a total of 361,194 possible 

management scenarios were evaluated. Evaluation was based on several important criteria 

such as percent change in turtle take by species, percent of fishing activity (longline gear 

sets) disrupted by the management action (i.e., static), percent of fishing activity lost after 

reallocation (i.e., dynamic), and percent change in fish catch revenue. For simplicity all 

scenarios were first partitioned into bins (e.g., 0%-5%, 5%-10%, 10%-15%, etc.) based 

upon values of the dynamic percent change in the take of leatherback turtles since this was 

the primary species of concern. Within these bins of leatherback take, the results were 

further sorted to discover optimal scenarios based on the criteria mentioned above, 

particularly the static value of fishing effort impact. This value of fishing effort disruption 

is an attractive criterion for gauging the impact of a time-area closure because it makes no 

assumptions about reallocation of lost fishing effort and is, therefore, useful in comparing 

different types of closure scenarios. We feel that fishing effort disrupted by a closure 

provides a basic measure of the impact of the closure because while it provides a measure 

of the fraction of effort impacted by the closure, it stops short of making further 

assumptions about how the fleet responds to that disruption and the economic impacts of 

this hypothetical response. At present, we are not confident we can model the fleet 

response and resulting economics with sufficient accuracy to determine the best closures; 

hence, fishing effort disruption remains the index value of choice. Several multicriteria 

optimizations were also attempted, such as simply summing static percent fishing activity 

lost and dynamic percent fishing activity lost to form a sum of percents. This particular 

optimization would search for a scenario with a minimal combined effect of disruption of 

fishing activity and net loss of fishing activity after adjustment to a seasonal or spatial 

closure. Another multicriteria optimization summed the individual turtle species’ take 

change together. This optimization would search for a scenario that best reduced the take 

of all turtle species, in an equally weighted fashion. A larger multicriteria optimization was 

formed by appropriately combining the two previously mentioned optimizations while 

paying close attention to arithmetic sign, so that the final criterion would both minimize 

disruption/loss while maximizing aggregate take reduction. For a given optimization, there 

were often many scenarios that nearly equally well met the optimization criteria, even at 

the resolution of whole degrees of latitude/longitude and whole months of time. The 

output from these exercises is voluminous, and it is difficult to select a clearly superior 

solution for a given optimization. Scenarios that differ in only a few percentage points are 

probably not significantly different from each other based upon some preliminary analyses 

of variability. For this reason, many scenarios should be evaluated together with additional 

input and criteria from fishermen, industry, and other concerned parties. 

Variability of predicted turtle take was estimated by constructing 95% variability 

bands around the values of interest using a randomization bootstrap procedure (Efron & 

Gong, 1983). In this procedure, individual longline sets in the observer database were 

randomly resampled with replacement to construct a new database of the original size. The 

GAMs were refitted with this new dataset and a new fleet-wide set of predicted turtle takes 

generated. This process was repeated 100 times and the distributions of the final values 

were used to address variability. The nonparametric or empirical variability bands were 
7

constructed by sorting the values from low to high and using the 2.5%th and the 97.5%th

values to identify the medial 95% of the distribution. This approach was also used to 

explore variability of the GAM smoother functions as described earlier and was here used 

to address variability of the optimal management scenarios for reducing leatherback sea 

turtle take. 

 The predicted effect of the emergency closure was examined using the same fleetwide turtle take database. This closure went into effect in November 1999 and closed the 

area north of latitude 28°N and between longitude 168°W and 150°W. The effects on turtle 

take, fishing effort, fish catch, and fish catch revenue were examined for this proposal 

under both static and dynamic reallocation modes. These results were compared to the 

results from the optimizations. 

 

Lastly, the GAMs were refitted using a substantially larger amount of observer data 

(12,688 sets vs initial 2,812 sets), made available from a longer time span of data 

collection and a sharply increased observer coverage in the longline fleet during recent 

years (Table 1). This represented an approximately 4.5X increase in the sample size. Each 

GAM was refit using the augmented data and monthly, latitudinal, and longitudinal 

summaries were compared to the initial analysis. 

RESULTS AND DISCUSSION 

 The results of this report will focus on optimal time-area closures with emphasis on 

leatherback turtles. The exploratory graphical analyses (Figs. 6 and 7) indicated that April 

and May accounted for the highest monthly leatherback takes, with a relatively widespread 

spatial distribution. This is in contrast to olive ridleys and especially loggerheads, which 

have relatively well defined latitudinal ranges. Olive ridleys tend to be taken more in the 

southerly regions of the fishing grounds, while loggerheads are primarily taken in the 

northerly regions. 

 

 Results from all scenarios are plotted graphically in Figure 8, using leatherback 

turtle take on the x-axis, and fishing effort disruption on the y-axis. This method of 

presenting the data is very similar to the “efficient frontier,” a financial concept used in 

Modern Portfolio Theory (Markowitz, 1991). The graph displays an envelope of points 

representing the hyperspace of possible outcomes. The efficient frontier is the point at 

which one quantity of interest is optimized at some preset value of another quantity of 

interest (in finance the plot would be of risk versus return). In Figure 8, fishing effort 

disruption is the quantity to be optimized, and the efficient frontier is the trace of points at 

the highest elevation for a given value of leatherback turtle take. Further examination of 

the management scenarios focused upon values along several of these efficient frontiers. 

The five best scenarios per take reduction bin for each of the 10 different types of 

management regimes are broken down in Figure 9 for leatherback turtle take and fishing 

effort disruption. This shows the performance and capability of various types of time or 
8

area closures. Note that to achieve optimal solutions at high levels of turtle take reduction, 

separated mode combinations of seasonal and spatial closures are required. At lower levels 

of turtle take reduction, simpler merged-mode combinations or spatial closures only may 

be adequate. Generally, combinations of seasonal and spatial closures provide the best 

solutions when targeting a particular level of turtle take reduction, at least when best is 

defined as having the least disruption of fishing effort. 

The efficient frontier margin for leatherbacks, estimated with a high degree 

polynomial fit to 1% bin points, is shown in Figure 10. Variability along the leatherback 

turtle efficient frontier is shown in Figure 11, where each best scenario per 1% take bin is 

bootstrapped 100 times, with the medial 95% of values bounded by a variability band. 

This approach will allow construction of nonparametric or empirical variability bands 

around not only the efficient frontier but also any specific output for a given management 

scenario. Revenue values from the efficient frontier for fishing effort disruption are shown 

in Figure 12. Note that this plot does not show an efficient frontier since revenue was not 

the value under optimization (earlier attempts at this approach were unsuccessful due to 

unstable boundary solutions; i.e., proposing nearly 100% fishing effort disruption to inflate 

low sample size revenue values). This figure is presented to indicate how revenue is 

predicted to change for targeted levels of leatherback turtle take reduction at optimal 

values of fishing effort disruption. This is only revenue changes from changes in fish 

catch quantity and composition and does not include potential additional costs associated 

with compliance to a management scenario (e.g., transit costs, loss of fishing days). 

Figures 13-15 show how the takes of other turtle species change along the efficient frontier 

for leatherback turtle take and fishing effort disruption. These are also not true efficient 

frontiers since the take rates for loggerhead, olive ridley, and green were not optimized. 

These show predicted changes in take for other turtle species along the leatherback 

efficient frontier with respect to optimized fishing effort disruption. Figure 13 is 

particularly interesting because it highlights some closures that reduce leatherback takes 

and also substantially reduce loggerhead takes. Specifically for the closures that reduce 

leatherback takes by 20%-30% and by 50%-60% there are some closures that also reduce 

loggerhead takes by 40%-55% (Fig. 13). Additionally, the true efficient frontiers of other 

turtle takes are plotted on Figures 13-15; these are easily distinguished from the above by 

their relative smoothness and magnitude of turtle take reductions. These scenarios, 

however, tend to disrupt the fishery to a greater extent. 

Table 3 shows the “best” results for all 19 bins by 5% leatherback turtle take 

reduction. The optimization criterion in choosing these scenarios was to minimize 

disruption of fishing effort. Other criteria, including multicriteria, were attempted, but 

until they are better refined the simple disruption of fishing effort appears to provide the 

best optimization index. Some problems with the multicriteria optimizations included high 

fishing effort disruption balanced by an assumed complete spatial reallocation to a small 

area; this optimization needs to be constrained to minimize disruption and loss separately. 

Summing these two changes resulted in lost information content. The aggregated turtle 

take reduction optimization suffered from excessive weighting toward loggerhead 

solutions since these were the most easily reduced; again, summing the take changes 
9

resulted in lost information content, and a penalty function for asymmetry may be the 

solution. For the fishing effort disruption criterion, even this small subset of the simulation 

output comprises 19 management scenarios, each one worthy of consideration for a 

particular level of targeted leatherback turtle take. These 19 scenarios represent the most 

efficient management actions to consider with regard to leatherback turtle protection. It is 

important to emphasize that many other unpresented scenarios may differ in only a degree 

of latitude or a degree of longitude or a single month yet fall upon a similar location on the 

efficient frontier. These subtle differences in management tactic may be very important 

from the perspective of a longline fisherman. For example, the transit time/expense for a 

single degree of latitude/longitude is nontrivial. If travel time increases by only a single 

day per trip, the net loss to a fisherman was estimated to be $4,000 per year in 1993 

(Hamilton et al., 1996). The loss of a single day of fishing (i.e., approximately one 

longline set) per trip was estimated to incur an annual cost of $16,000. 

 

 The predicted effects of the emergency closure for turtle take effects and fish catch 

effects are summarized in Tables 4A and 4B. Since this is a strictly spatial closure, the 

simulation reallocated 100% of the disrupted fishing effort to areas outside of the closure 

during the same months and same trip-type effort. Fish catch revenue references the total 

change using the entire catch, including many additional species not listed in the table. 

The effects are broken down by year and a multiyear average is presented at the bottom of 

each table. This scenario is predicted to be somewhat detrimental to olive ridley and green 

turtles, while producing a relatively small protective effect for leatherback turtles, the 

species for which it was originally intended. It disrupts approximately 13% of the fishing 

effort with a small decrease in fish catch revenue. The emergency closure scenario is quite 

distant to the efficient frontier for all species, when compared to other management 

scenarios found in Table 3 at this level of turtle take reduction. The symbol “E” is plotted 

on Figures 9-10 to indicate the predicted location of the emergency closure in comparison 

to the other scenarios. Table 5 lists 25 management scenarios that disrupt longline fishery 

effort to approximately the same extent as the emergency closure, but with more effective 

leatherback turtle take reduction. 

 

 The GAM analyses appeared to be robust for all species since the monthly, 

latitudinal, and longitudinal summaries were similar between the initial runs and the 4.5X 

augmented data runs. This suggests that the spatial and temporal patterns identified in the 

initial modeling persisted as the sample size of the observer database grew from 2,812 sets 

to 12,688 sets. In particular, features such as the March-April pulse of leatherback takes 

appeared in both analyses (Fig. 16). Other spatial patterns in latitude (Fig. 17) and 

longitude (Fig. 18) were retained in both sets of take predictions, with a commensurate 

narrowing of the bootstrapped 95% variability bands as would be expected with a larger 

sample size. 

 

 In conclusion, it has been shown that time-area closures are a viable option even in 

complex situations with multiple species of concern. The compromises in take reductions 

and impacts to fishermen can be quantitatively examined in a rigorous framework, which 

would assist fishery managers and protected resource managers in reaching consensus on 
10

effective management measures acceptable to all constituents. The use of GAMs in 

processing observer data to identify important spatial and temporal patterns of sea turtle 

take is promising, and results appear to be robust with respect to sample size concerns. 

Further work in modeling pelagic movement as well as fishing gear characteristics will be 

a useful tool to assist in take mitigation. 

ACKNOWLEDGMENTS 

We thank Sam Pooley for providing the data necessary to estimate revenue impacts 

in the time/area closure simulations, and for preparing the paragraph describing the data. 

Pierre Kleiber provided much-appreciated assistance with S-Plus GAM analysis and 

database preparation. We also thank Kathy Cousins, Charles Karnella, Pierre Kleiber, 

Mike Laurs, Marilyn Luipold, Alec MacCall, Marti McCracken, Rod McInnis, Dick Neal, 

Sam Pooley, Barbara Schroeder, Mike Tillman, and Jerry Wetherall for their helpful 

advice and reviews of earlier drafts of this report. The Time/Area Review Panel members 

Larry Crowder, John Hampton, and Mike Sissenwine offered helpful advice and 

constructive criticism as well. 
11

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15

Table 1. Summary of Hawaii-based longline fishing activity and observer coverage 

1991-2003 (from NMFS PIFSC Fishery Monitoring and Economics Program and 

NMFS PIRO observer program). 

Year Vessels Trips Sets Observed sets Percent 

observed 

1991 141 1686 12632 0 0.00%

1992 123 1308 11546 0 0.00%

1993 122 1233 12318 0 0.00%

1994 125 1105 10799 509 4.71% 

1995 110 1170 11732 549 4.68%

1996 104 1137 11638 642 5.52%

1997 105 1162 11846 498 4.20%

1998 115 1181 12507 591 4.73%

1999 122 1165 12805 460 3.59%

2000 125 1135 12930 1427 11.04%

2001 101 1075 12169 2803 23.03%

2002 102 1193 13911 3504 25.19%

2003 110 1216 14560 3254 22.35%

1991-2003 

Average 116 1213 12415 1095 8.39% 

1994-1999 

Average 114 1153 11888 542 4.57% 

2000-2003 

Average 110 1155 13393 2747 20.40% 
16

Table 2. Summary of annual sea turtle take and mortality in the Hawaii-based 

longline fishery 1994-2002 (sea turtle takes from McCracken, personal 

communication; sea turtle mortality from Wetherall, personal communication). 

Loggerhead Leatherback Olive Ridley Green Year 

Takes Mortality Takes Mortality Takes Mortality Takes Mortality

1994 501 202 109 36 107 52 37 16 

1995 412 166 99 32 143 70 38 16 

1996 445 178 106 35 153 74 40 17 

1997 371 149 88 28 154 76 38 17 

1998 407 164 139 47 157 80 42 18 

1999 369 149 132 45 164 88 45 22 

2000 246 106 132 45 113 65 65 35 

2001 18 8 10 3 36 27 11 8 

2002 17 7 5 2 31 29 3 3 

1994-2002 Average 310 125 91 30 118 62 35 17 

1994-1999 Average 418 168 112 37 146 73 40 18 

2000-2002 Average 94 40 49 17 60 40 26 15 
17

Table 3. “Best” scenarios optimized for minimal fishing effort disruption per 5% bin of leatherback turtle take 

Notations: LTTR = leatherback turtle take reduction 5% bin upper bound, #Mn = the number of months for a closure, Ms = the starting month of a seasonal closure, Lon/Lat = position of spatial closure, FED = fishing efforFEL = fishing effort lost, Rev = fish catch revenue change, Log = loggerhead turtle take change, Lea = leatherbachange, Rid = olive ridley turtle take change, Gre = green turtle take change, Swo = swordfish catch change, Big 

catch change. Other fish species included in the revenue calculations are not presented. 

LTTR 

Type of 

management 

action #Mn Ms Lon/Lat FED FEL Rev Log Lea Rid Gre 

-95% 

Separated 

season 

latitude 9 11 37N -83.74% -73.03% -80.50% -90.17% -95.69% -59.98% -77.72% 

-90% 

Separated 

season 

latitude 8 12 33N -76.98% -64.30% -74.84% -94.10% -90.50% -48.62% -72.00% 

-85% 

Separated 

season box 9 4 150W/32N -73.13% -63.69% -55.21% -36.31% -85.39% -74.87% -63.31% 

-80% 

Separated 

season 

latitude 4 4 24N -58.84% -26.95% -36.91% -92.03% -80.44% 7.33% -40.79% 

-75% 

Separated 

season 

latitude 4 4 31N -44.74% -26.95% -25.80% -46.54% -75.00% -21.38% -33.81% 

-70% 

Separated 

season box 4 4 173W/33N -41.08% -26.95% -24.90% -16.17% -70.08% -25.78% -27.43% 

-65% 

Separated 

season box 4 4 157W/33N -38.72% -26.95% -25.89% 0.12% -65.00% -27.79% -30.01% 

-60% 

Separated 

season 

latitude 3 4 32N -35.79% -19.00% -19.83% -37.12% -61.90% -10.68% -22.69% 

-55% 

Separated 

season box 3 4 165W/33N -32.95% -19.00% -19.71% -15.08% -55.15% -14.54% -18.32% 

-50% 

Separated 

season 

latitude 2 4 33N -26.22% -10.04% -10.69% -32.72% -50.68% -2.57% -7.33% 
18

Table 3. (Continued) 

LTTR 

Type of 

management 

action #Mn Ms Lon/Lat FED FEL Rev Log Lea Rid Gre 

-45% Separated 

season box 2 4 171W/34N -23.93% -10.04% -10.25% -16.39% -45.01% -5.82% -2.59% 

-40% 

Separated 

season box 2 4 151W/34N -21.81% -10.04% -10.84% -2.98% -40.29% -7.23% -4.41% 

-35% 

Separated 

season box 2 4 154W/38N -20.37% -10.04% -10.96% -2.42% -35.02% -7.64% -5.80% 

-30% Season 2 4 - -20.07% -10.04% -11.02% -2.19% -34.23% -7.78% -6.22% -25% 

Separated 

season 

latitude 1 4 33N -16.15% 0.00% 0.57% -32.68% -25.48% 6.14% -0.46% 

-20% 

Merged 

season 

latitude 11 9 31N -8.62% 0.00% 0.88% -35.26% -20.03% 5.84% 2.26% 

-15% 

Merged 

season 

latitude 4 9 31N -4.89% 0.00% 1.58% -20.19% -15.06% 5.05% -1.24% 

-10% 

Merged 

season box 2 11 170W/33N -2.81% 0.00% 0.71% -10.29% -10.11% 1.44% 1.90% 

-5% 

Merged 

season box 1 12 160W/33N -1.12% 0.00% 0.18% -0.48% -5.03% 0.39% 0.96% 
19

Table 4A. Predicted impact of court-ordered emergency closure on sea turtle take in 

the Hawaii-based longline fishery 1994-1998. The first percentage is the static case 

assuming the fishery does not respond, the second percentage in parentheses is the 

dynamic case assuming the fishery can reallocate fishing effort. 

Year Loggerhead Leatherback Olive ridley Green Fishing effort 

1994 -48% (-13%) -37% (-14%) -6% (6%) -10% (46%) -19% (0%) 

1995 -42% (-21%) -19% (-3%) -5% (1%) -8% (1%) -9% (0%) 

1996 -60% (-33%) -21% (-1%) -3% (6%) -13% (0%) -14% (0%) 

1997 -66% (-27%) -19% (-1%) -3% (2%) -11% (0%) -13% (0%) 

1998 -42% (-17%) -14% (-3%) -5% (2%) -10% (-1%) -11% (0%) 

Average -51% (-22%) -22% (-4%) -4% (3%) -11% (9%) -13% (0%) 
20

Table 4B. Predicted impact of court-ordered emergency closure on selected fish 

catch and total fish catch revenue in the Hawaii-based longline fishery 1994-1998. 

The first percentage is the static case assuming the fishery does not respond, the 

second percentage in parentheses is the dynamic case assuming the fishery can 

reallocate fishing effort. 

Year Swordfish Bigeye tuna Albacore tuna Yellowfin tuna Fish catch 

revenue 

1990 -4% (0%) -1% (1%) -0% (0%) -1% (1%) -1% (1%) 

1991 -49% (-18%) -16% (3%) -38% (-25%) -17% (1%) -33% (-6%) 

1992 -52% (-6%) -14% (11%) -56% (-34%) -29% (-2%) -40% (2%) 

1993 -42% (-9%) -15% (7%) -25% (2%) -14% (5%) -29% (-1%) 

1994 -46% (-6%) -5% (2%) -21% (-2%) -6% (4%) -23% (-1%) 

1995 -25% (-5%) -4% (2%) -6% (-1%) -5% (2%) -11% (0%) 

1996 -45% (-22%) -4% (10%) -8% (-1%) -8% (9%) -17% (1%) 

1997 -42% (-9%) -4% (2%) -4% (0%) -6% (1%) -15% (-1%) 

1998 -29% (-5%) -6% (1%) -5% (1%) -6% (1%) -12% (0%) 

1999 -31% (-5%) -7% (1%) -6% (1%) -11% (-4%) -15% (-2%) 

Average -37% (-9%) -8% (4%) -17% (-6%) -10% (2%) -20% (-1%) 
21

Table 5. Listing of twenty-five best scenarios for fishing effort disruption approximating Emergency clos

10% to -15%) optimized for leatherback turtle take reduction. Notations: #Mn = the number of months closure, Ms = the starting month of a seasonal closure, Lon/Lat = position of spatial closure, FED = fishindisruption, FEL = fishing effort lost, Rev = fish catch revenue change, Log = loggerhead turtle take chanleatherback turtle take change, Rid = olive ridley turtle take change, Gre = green turtle take change, Swo catch change, Big = bigeye tuna catch change. Other fish species included in the revenue calculations are Results are not presented in any ordered sequence. 

Type of 

management 

action #Mn Ms Lon/Lat FED FEL Rev Log Lea Rid Gre 

Latitude 0 0 30N -13.54% 0.00% -0.26% -49.88% -22.23% 8.15% 2.82% 

Separated 

season 

latitude 1 4 34N -14.77% 0.00% 0.21% -26.41% -22.18% 5.08% -0.96% 

Separated 

season box 1 4 170W/33N -14.93% 0.00% 1.27% -20.67% -22.46% 3.44% 5.75% 

Separated 

season box 1 4 169W/33N -14.80% 0.00% 1.15% -19.85% -22.18% 3.32% 5.71% 

Separated 

season box 1 4 168W/33N -14.74% 0.00% 1.08% -19.70% -22.17% 3.25% 5.59% 

Merged 

season 

latitude 7 7 26N -13.54% 0.00% -0.89% -54.36% -22.22% 16.00% -11.67% 

Merged 

season 

latitude 9 4 28N -13.60% 0.00% 0.51% -35.22% -23.67% 9.71% -8.45% 

Merged 

season 

latitude 9 5 27N -14.75% 0.00% -0.08% -52.63% -23.93% 11.51% -12.23% 

Merged 

season 

latitude 9 5 28N -12.64% 0.00% -0.02% -51.39% -23.05% 9.90% -8.99% 

Merged 

season 

latitude 9 6 28N -14.93% 0.00% -1.48% -64.37% -22.39% 10.57% -8.80% 
22

Table 5. (Continued) 

Type of 

management 

action #Mn Ms Lon/Lat FED FEL Rev Log Lea Rid Gre 

Merged 

season 

latitude 10 4 29N -12.91% 0.00% -0.04% -50.05% -23.95% 8.97% -4.85% 

Merged 

season 

latitude 10 5 29N -13.91% 0.00% -0.14% -57.96% -22.83% 9.34% -4.51% 

Merged 

season 

latitude 11 9 30N -12.55% 0.00% 0.12% -42.43% -22.18% 7.22% 4.91% 

Merged 

season box 10 4 166W/26N -14.49% 0.00% 0.35% -29.25% -22.60% 5.38% 14.45% 

Merged 

season box 10 4 171W/27N -14.89% 0.00% 0.28% -35.30% -23.89% 5.83% 15.17% 

Merged 

season box 10 4 170W/27N -14.39% 0.00% 0.26% -34.44% -23.45% 5.58% 14.77% 

Merged 

season box 10 4 169W/27N -13.88% 0.00% 0.34% -32.68% -22.68% 5.30% 14.25% 

Merged 

season box 10 4 168W/27N -13.36% 0.00% 0.57% -31.79% -22.58% 5.08% 13.69% 

Merged 

season box 10 4 167W/27N -12.93% 0.00% 0.67% -30.86% -22.30% 4.93% 13.05% 

Merged 

season box 10 4 173W/28N -13.60% 0.00% 0.45% -35.59% -22.76% 5.53% 12.85% 

Merged 

season box 10 4 172W/28N -13.04% 0.00% 0.38% -35.30% -22.60% 5.23% 13.16% 

Merged 

season box 10 4 171W/28N -12.68% 0.00% 0.37% -34.86% -22.49% 5.05% 13.07% 

Merged 

season box 11 3 167W/28N -14.91% 0.00% 0.93% -32.70% -22.21% 5.54% 24.00% 

Merged 

season box 11 4 169W/28N -14.64% 0.00% -0.21% -43.90% -22.42% 5.24% 16.28% 

Merged 

season box 11 4 168W/28N -14.24% 0.00% 0.04% -42.67% -22.35% 5.06% 16.02% 
23

Figure 1. Map of Hawaii-based longline fishing effort (1994-1999). 
24

Figure 2. Smoother functions for loggerhead turtle GAM, with bootstrapped 95% variability bands.
25

Figure 3. Smoother functions for leatherback turtle GAM, with bootstrapped 95% variability bands.
26

Figure 4. Smoother functions for olive ridley turtle GAM, with bootstrapped 95% variability bands.

Figure 5. Smoother functions for green turtle GAM, with bootstrapped 95% variability bands. 
27

Turtle take in longline fishery by month (1994-1998)

Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Percent in monthly bin

0

5

10

15

20

25 Loggerhead

Leatherback

Olive Ridley

Green

Figure 6. Turtle take in the Hawaii-based longline fishery by month, 1994-1998. 
28

Turtle take in longline fishery by latitude (1994-1998)

Latitude

0N 5N 10N 15N 20N 25N 30N 35N 40N 45N 50N

Percent in one degree bin

0

2

4

6

8

10

12

14

16

18

Loggerhead

Leatherback

Olive Ridley

Green

Turtle take in longline fishery by longitude (1994-1998)

Longitude

170E 175E 180 175W 170W 165W 160W 155W 150W 145W 140W 135W

Percent in one degree bin

0

2

4

6

8

10

12

Loggerhead

Leatherback

Olive Ridley

Green

Figure 7. Turtle take in the Hawaii-based longline fishery by latitude and longitude, 1994-1998. 
29

Figure 8. All management scenarios evaluated in the leatherback turtle take reduction simulations, 

showing changes in turtle take and fishing effort disruption.
30

Five best scenarios per 5% take bin per type of regime

Leatherback turtle take change (percent)

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0

Fishing effort disruption (percent)

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

1 1

1 1

1 1 11

1

1

1

1

1

1

1

1

1

1 1

1

1

1

1

1

1

1

1

1

1

1

1

1

1 1

1

1

1 1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1 1

1

1

1

1

1

1

1 1

1

1

11

1

1

1

1

1

1

1

1 1

11

1

2

2 2 2 2

2

2

2 2 2

2

2

2

2

2

3 3

3

3 3

3

3

3

3

3

3

3

3

3

3

3

44444

4 444

4 444

4

4 4

4 4 44

4 4 4

4 4 44

4

4 4 4

4

4

4

4

5 5555

55 555

555

55 5 5

5

5

5

5

5

5

5 55 5

5

5

5

5 5

5

55 5

5

5

6666

66

6666

666 6

6

6

66

6

6

6 6

6 6

6

6

6

6

6

6

7

77

777

77 7 7

7

7

7 7

77

7 77 7

7

7

7

7 7

7

77 7

7

7

8 8 8 8 8

8 8 8 8

8

8 8 8 8

8

8

8 8

88

8 8

8

8

8

8 8

8 8

8

8

8 8 8 8

88 8 8 8

8

8

8 88

8

8

88 8

8

8

888

8 8 88

8

8

8

8

88

8

8

8

8 8

8

8

8

88

8 888

8

88

88

8888

8

9 9 99

9

9 9 9 9 9

9 9 9

9

9

9

9

9 9

9

9

9 9

9

9

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9 9

9

9 9 99

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9 9

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9 9 9

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9 9 9

9

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9 9

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9 9

9

9 9

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9

9

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9 9

9 9

99

999

999

10101010

1010

1010

1010

10 1010

10

1010

1010

1010

10 10

10

101010

10 10 101010

10101010

101010

10 10

10 10

10

10

10

10

10

10

10

1010101010

101010

E

1 Season

2 Longitude

3 Latitude

4 Merged season longitude

5 Merged season latitude

6 Box

7 Merged season box

8 Separated season longitude

9 Separated season latitude

10 Separated season box

E Emergency closure

Figure 9. Best management scenarios evaluated in leatherback turtle take reduction simulations, 

broken down by type of management regime, changes in turtle take and fishing effort disruption. The 

“E” denotes the predicted location of the emergency closure. 
31

"Efficient frontier" for leatherback turtles

Leatherback turtle take change (percent)

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10

Fishing effort disruption (percent)

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

E

Figure 10. Polynomial representation of the efficient frontier for leatherback turtles. Points represent 

best values per 1% bin of turtle take reduction. The “E” denotes the predicted location of the 

emergency closure. 
32

Variability of "efficient frontier" for leatherback turtles

Leatherback turtle take change (percent)

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0

Fishing effort disruption (percent)

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

Figure 11. 95% variability envelope of the efficient frontier for leatherback turtle take reduction and 

fishing effort disruption from bootstrapping. 
33

"Revenue frontier" for leatherback turtles

Leatherback turtle take change (percent)

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10

Fish catch revenue change (percent)

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

10

Figure 12. Polynomial representation of the revenue frontier for leatherback turtles. This represents 

the changes in fish catch revenue associated with scenarios optimized with respect to fishing effort 

disruption. 
34

"Loggerhead frontier" for leatherback turtles

Leatherback turtle take change (percent)

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10

Loggerhead turtle take change (percent)

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

10

Figure 13. Polynomial representations of the loggerhead turtle take frontier for leatherback turtles. 

Upper data represents changes in loggerhead turtle take associated with scenarios optimized with 

fishing effort disruption. Lower data optimized only for loggerhead turtle take reduction. 
35

"Olive ridley frontier" for leatherback turtles

Leatherback turtle take change (percent)

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10

Olive ridley turtle take change (percent)

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

Figure 14. Polynomial representations of the olive ridley turtle take frontier for leatherback turtles. 

Upper data represents changes in olive ridley turtle take associated with scenarios optimized with 

fishing effort disruption. Lower data optimized only for olive ridley turtle take reduction. 
36

"Green frontier" for leatherback turtles

Leatherback turtle take change (percent)

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10

Green turtle take change (percent)

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

Figure 15. Polynomial representations of the green turtle take frontier for leatherback turtles. Upper 

data represents changes in green turtle take associated with scenarios optimized with fishing effort 

disruption. Lower data are optimized only for green turtle take reduction. 
37

Figure 16. Sea turtle take (1994-1999) summarized by month for each species. The initial GAM 

predictions are on the left (n=2,812 sets) and the augmented-data GAM predictions are on the right 

(n=12,688 sets). 
38

Figure 17. Sea turtle take (1994-1999) summarized by latitude for each species. The initial GAM 

predictions are on the left (n=2,812 sets) and the augmented-data GAM predictions are on the right 

(n=12,688 sets). 
39

Figure 18. Sea turtle take (1994-1999) summarized by longitude for each species. The initial GAM 

predictions are on the left (n=2,812 sets) and the augmented-data GAM predictions are on the right 

(n=12,688 sets).