7. “Best” Abundance Estimates and Best Management
107
Conclusion
Given the objective of preventing population declines while minimizing restrictions on fisheries, this modeling exercise has shown that including uncertainty in
the management regime (the N MIN strategy) is superior. Abundance estimation is
difficult for many marine mammal species (Table 7.1). Management objectives
are not met for species with high CVs using the N MEAN strategy. If CVs could be
reduced to low levels for all species, the N MIN and N MEAN strategies would be
similar. Unfortunately, it is often difficult to reduce CVs. As population size
decreases, CVs increase (Taylor and Gerrodette 1993). Therefore, threatened,
endangered, or depleted populations may be managed poorly by using N MEAN .
Species that are difficult to sight (e.g., those with long dive times, that surface with
little splashing and no visible blow) may also suffer from chronically high CVs
and therefore poor management by using N MEAN . Using N MEAN can also contribute
to economic uncertainty for fisheries. For example, the probability of depleting a
population is much higher using the N MEAN strategy, which could severely affect
fisheries if closures were required for population recovery.
The creation of a model to meet management objectives must consider the data
available (past, present, and future). Species managed by the International Whaling Commission have historical catch data that allow a calculation of population
status, and a more sophisticated model was created to use these data (Cooke
1994). Such data are not available for most marine mammals in U.S. waters.
Given less data, a model that requires less data to drive the management regime is
appropriate. Although the models must differ, the principle of creating a model to
meet performance standards based on quantitative management objectives remains sound.
Acknowledgments. This research was supported with a National Research Council Associateship (Taylor) and by the National Marine Fisheries Service’s Office
of Protected Resources (Wade). Improvements were made thanks to reviews by
Jay Barlow, Robert Brownell, Doug DeMaster, Tim Gerrodette, Lloyd Lowery,
Robert Hofman, Steve Reilly, and Michael Tillman.
Literature Cited
Barlow J (1993) The abundance of cetaceans in California waters estimated from ship
surveys in summer/fall 1991. Administrative Report LJ-93-09. National Marine Fisheries Service. Southwest Fisheries Science Center, La Jolla, CA
Barlow J, Hanan D (1995) An assessment of the status of harbor porpoise in central
California. Report of the International Whaling Commission, Special Issue 16:123–140
Buckland ST, Anderson DR, Burnham KP, Laake JL (1993) Distance sampling: estimating
abundance of biological populations. Chapman and Hall, London
Cooke JG (1994) The management of whaling. Aquatic Mammals 20:129–135
Donovan GP (1989) The comprehensive assessment of whale stocks: the early years.
Report of the International Whaling Commission, Special Issue 11
107
Conclusion
Given the objective of preventing population declines while minimizing restrictions on fisheries, this modeling exercise has shown that including uncertainty in
the management regime (the N MIN strategy) is superior. Abundance estimation is
difficult for many marine mammal species (Table 7.1). Management objectives
are not met for species with high CVs using the N MEAN strategy. If CVs could be
reduced to low levels for all species, the N MIN and N MEAN strategies would be
similar. Unfortunately, it is often difficult to reduce CVs. As population size
decreases, CVs increase (Taylor and Gerrodette 1993). Therefore, threatened,
endangered, or depleted populations may be managed poorly by using N MEAN .
Species that are difficult to sight (e.g., those with long dive times, that surface with
little splashing and no visible blow) may also suffer from chronically high CVs
and therefore poor management by using N MEAN . Using N MEAN can also contribute
to economic uncertainty for fisheries. For example, the probability of depleting a
population is much higher using the N MEAN strategy, which could severely affect
fisheries if closures were required for population recovery.
The creation of a model to meet management objectives must consider the data
available (past, present, and future). Species managed by the International Whaling Commission have historical catch data that allow a calculation of population
status, and a more sophisticated model was created to use these data (Cooke
1994). Such data are not available for most marine mammals in U.S. waters.
Given less data, a model that requires less data to drive the management regime is
appropriate. Although the models must differ, the principle of creating a model to
meet performance standards based on quantitative management objectives remains sound.
Acknowledgments. This research was supported with a National Research Council Associateship (Taylor) and by the National Marine Fisheries Service’s Office
of Protected Resources (Wade). Improvements were made thanks to reviews by
Jay Barlow, Robert Brownell, Doug DeMaster, Tim Gerrodette, Lloyd Lowery,
Robert Hofman, Steve Reilly, and Michael Tillman.
Literature Cited
Barlow J (1993) The abundance of cetaceans in California waters estimated from ship
surveys in summer/fall 1991. Administrative Report LJ-93-09. National Marine Fisheries Service. Southwest Fisheries Science Center, La Jolla, CA
Barlow J, Hanan D (1995) An assessment of the status of harbor porpoise in central
California. Report of the International Whaling Commission, Special Issue 16:123–140
Buckland ST, Anderson DR, Burnham KP, Laake JL (1993) Distance sampling: estimating
abundance of biological populations. Chapman and Hall, London
Cooke JG (1994) The management of whaling. Aquatic Mammals 20:129–135
Donovan GP (1989) The comprehensive assessment of whale stocks: the early years.
Report of the International Whaling Commission, Special Issue 11