A Kalman Filter for Environmental Risk: Spatio-temporal Variation in Sea Turtle Bycatch Rates
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چکیده
We extend a Kalman filter model for count data to reflect observations irregularly spaced in time. The model is applied to estimate risk of rare events; bycatch of endangered leatherback turtles in a swordfish fishery off Oregon and California. Incidental takes of turtles are modeled as a Poisson process. The results suggest that take risk is better estimated by regarding the entire history of observations, contrary to only considering data from the previous season as in the current management regime. The conservatory measures currently enforced in the fishery may be overly stringent. JEL Classification: Q22, Q28, Q57, Q58.
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