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Reducing bias and improving precision in species extinction forecasts.

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    Accurate population viability analyses (PVA) are vital for conservation. Replicated observations at each time step improve extinction risk estimates when process error is high, but offer little benefit when process error is low.

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    Area of Science:

    • Ecology
    • Conservation Biology
    • Population Dynamics

    Background:

    • Population viability analyses (PVA) are essential for predicting species decline and informing conservation strategies.
    • Time-series count data is commonly used for PVA, but longer observation periods are often impractical.
    • Replicated observations within a single time step offer an alternative to extending observation periods for data collection.

    Purpose of the Study:

    • To investigate the trade-off between observation period length and the number of observations for population viability analyses.
    • To determine the optimal data collection strategy for improving the precision of extinction risk estimates.

    Main Methods:

    • Analysis of simulated population count data.
    • Examination of the relationship between observation period length, total observations, and estimation precision.
    • Evaluation of the impact of the process error to measurement error variance ratio on estimation accuracy.

    Main Results:

    • When the ratio of process error to measurement error variance is high, replicated observations at each time step significantly enhance the precision of quasi-extinction risk estimates.
    • Conversely, when this ratio is low, replicated observations provide minimal improvement in precision.
    • The findings highlight the importance of the error structure in population data for designing effective monitoring.

    Conclusions:

    • The optimal design for population monitoring depends on the relative magnitudes of process and measurement error.
    • Replicated sampling is most beneficial for improving PVA precision in populations with high process error.
    • These results provide guidance for designing efficient monitoring schemes for species conservation.