Research Article: Using an individual-based model to assess common biases in lek-based count data to estimate population trajectories of lesser prairie-chickens

Date Published: May 17, 2019

Publisher: Public Library of Science

Author(s): Beth E. Ross, Daniel S. Sullins, David A. Haukos, Karen Root.


Researchers and managers are often interested in monitoring the underlying state of a population (e.g., abundance), yet error in the observation process might mask underlying changes due to imperfect detection and availability for sampling. Additional heterogeneity can be introduced into a monitoring program when male-based surveys are used as an index for the total population. Often, male-based surveys are used for avian species, as males are conspicuous and more easily monitored than females. To determine if male-based lek surveys capture changes or trends in population abundance based on female survival and reproduction, we developed a virtual ecologist approach using the lesser prairie-chicken (Tympanuchus pallidicinctus) as an example. Our approach used an individual-based model to simulate lek counts based on female vital rate data, included models where detection and lek attendance probabilities were <1, and was analyzed using both unadjusted counts and an N-mixture model to compare estimates of population abundance and growth rates. Using lek counts to estimate population growth rates without accounting for detection probability or density-based lek attendance consistently biased population growth rates and abundance estimates. Our results therefore suggest that lek-based surveys used without accounting for lek attendance and detection probability may miss important trends in population changes. Rather than population-level inference, lek-based surveys not accounting for lek attendance and detection probability may instead be better for inferring broad-scale range shifts of lesser prairie-chicken populations in a presence/absence framework.

Partial Text

Monitoring fish and wildlife populations can be challenging. While managers are often interested in monitoring the underlying state of the system (e.g., abundance), error in the observation process might mask underlying population changes. Imperfect detection, availability for sampling, and heterogeneity in abundance can all mask underlying changes of abundance [1,2]. When using counts of abundance to estimate changes in population growth rate over time, sampling issues can cause biased inference. For example, if detection probability is not properly incorporated into estimates of population growth rate, even a small change (4–8%) in detectability between two treatments can lead to a 50–90% increase in committing a Type 1 error (detecting a difference when none exists; [3]).

In 29,557 of 30,000 simulations (100 simulations at 300 sites) of our base model (lek attendance probability and detection probability = 1; Scenario 1), we reached population extinction over a 25-year period with an initial population of 100 total birds (50 females, 50 males). All simulations ended with fewer than 10 birds, likely a pseudo-extinct population (Scenario 2; Fig 1).

The virtual ecologist approach allows for assessment of monitoring designs based on hypothesized population and observation processes. Given the common use of lek counts for monitoring lekking species, our virtual ecologist model has wide applicability. Generally, our results indicate that monitoring using lek counts alone may not fully capture persistent long-term declines in abundance when detection probability is <1, especially when density affects lek attendance rates. When accounting for detection probability with an N-mixture model, mean long-term population growth rates were underestimated, resulting in population growth rates below true values. Neither commonly used method to estimate long-term population growth rates was able to accurately capture population abundance until the population had decreased substantially (i.e., year 14).   Source:


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