How sampling affects estimates of demographic parameters
Article first published online: 17 MAY 2012
© 2012 International Association for Vegetation Science
Journal of Vegetation Science
Volume 23, Issue 6, pages 1170–1179, December 2012
How to Cite
Lesser, M. R., Brewer, S. (2012), How sampling affects estimates of demographic parameters. Journal of Vegetation Science, 23: 1170–1179. doi: 10.1111/j.1654-1103.2012.01419.x
- Issue published online: 7 NOV 2012
- Article first published online: 17 MAY 2012
- Manuscript Accepted: 26 MAR 2012
- Manuscript Received: 29 NOV 2011
- USDA McIntire-Stennis Competitive Grant
- NSF Doctoral Dissertation Improvement Grant. Grant Number: DEB-0910173
- NSF. Grant Number: EAR-1003848
- Logistic regression;
- Pinus ponderosa ;
- Population growth;
- Sampling error and bias;
- Sampling method;
- Simulation modelling
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?
North-central Wyoming, USA.
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.
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.
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.