SEARCH

SEARCH BY CITATION

Keywords:

  • Logistic regression;
  • Pinus ponderosa ;
  • Population growth;
  • Sampling error and bias;
  • Sampling method;
  • Simulation modelling

Abstract

Question

Demographic rates are often modelled using small data sets over short time frames. Here, we use fully sampled populations as a basis for testing how the intensity of two different sampling approaches (individual random-tree and n-tree distance plots) can affect estimates of growth parameters and the timing of population development. How do sampling method and intensity affect estimates of early stages of population growth?

Location

North-central Wyoming, USA.

Methods

We used a data set in which every individual in each of four discrete ponderosa pine populations was mapped and aged. We calculated cumulative population growth and fitted it to a logistic regression model. Based on this model, we estimated population growth rate, first colonization, timing of population growth initiation, maximum growth rate and growth saturation. We conducted simulations for two sampling methods. First, individual trees were chosen at random, with different percentages of the full population being chosen. Second, we simulated n-tree distance plot sampling, where we changed the number of plots that were laid in each population. For each method and at each intensity, 10 000 simulation runs were performed. The simulation results were fitted to a logistic regression model. We then looked at the difference between the full and partially sampled population results to examine how lowering sampling intensity affected the results.

Results

Population growth rate was not significantly affected by sampling intensity except at low levels of sampling. However, first colonization and timing of population initiation were affected by sampling intensity. For both parameters, the individual random-tree method produced more accurate results than the n-distance method as sampling intensity decreased.

Conclusions

Accurate estimation of population growth parameters is critical for both ecological understanding and resource management. Results are encouraging in that they indicate that moderate levels of sampling will reliably estimate population growth parameters. However, our results are specific to ponderosa pine and may not apply to other species with different life-history characteristics. Our results also highlight the fact that population structure can play a major role in sampling accuracy and needs to be considered in choosing the appropriate method and intensity.