Comparative analysis of plotless sampling methods for estimating woody plant density in a West African savanna agroforestry parkland

Agroforestry parklands are an age-old traditional land use practice that integrates crop cultivation under scattered woody plants. This practice is widespread in West African savannas providing many essential ecological and socio-economic benefits to people such as food, fuelwood, and medicine. Currently, parklands are decreasing due to changes in agriculture and land use practices, often associated with human population growth. Understanding spatial patterns as well as identifying reliable methods of sampling to estimate the density of woody plants is necessary for the sustainable management of parklands. In this study, a comparative analysis of select plotless sampling methods was performed using field and simulated datasets with known spatial patterns from field assessments. Results of spatial indices tests indicated that woody plants in parklands exhibited two spatial patterns: i.e., aggregate and random, the latter being the dominant pattern observed in field datasets. Based on relative measure statistics (i.e., RRMSE and RBIAS), the ordered distance (OD), point-centered quarter (PCQ), and closest individual (CI) methods performed well when woody plants were located in a random pattern while the variable area transect (VAT) method was better at estimating density under patterns of spatial aggregation. Overall, OD and VAT methods are recommended for density estimation in parklands because they are relatively more accurate, less biased, and practical and computations are easy to undertake.