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.

Comparing distance-based methods of measuring plant density in an arid sparse scrubland: testing field and simulated sampling

Plant density is an important indicator of rangeland monitoring and management. In this paper, we aimed at comparing distance-based methods of density measurement in field and in a simulated approach for two species of Astragalus verus Olivier and Astragalus albispinus Sirj. & Bornm with random and regular distribution pattern respectively. Then, we simulated a sampling scheme based on field measured statistics of the two species using Stochastic Geometry program and repeated the measurements using the same density methods. Moreover, the total individual plants of each species were counted in the sampling plots as the control method in Marjan rangelands of Iran during summer 2018. Results indicated that the best method for A. verus is nearest neighbor and point-centered quarter in field and simulated datasets in terms of both accuracy and precision respectively. For A. albispinus, the nearest neighbor and third closest individual were the best methods when compared with control in term of precision and accuracy in the field respectively and third closet individual in simulated dataset in terms of both accuracy and precision compared with control, respectively. The results reveal that there is no statistically significant difference between the nearest neighbor and point-centered quarter for A. verus and third closest individual and nearest neighbor for A. albispinus when field/simulated datasets are compared with each other and controls. Therefore, we recommend using these distance methods when this is a known species distribution pattern, basic number per unit, and dimensions of the unit as an alternative.

An Evaluation of Plotless Sampling Using Vegetation Simulations and Field Data from a Mangrove Forest

In vegetation science and forest management, tree density is often used as a variable. To determine the value of this variable, reliable field methods are necessary. When vegetation is sparse or not easily accessible, the use of sample plots is not feasible in the field. Therefore, plotless methods, like the Point Centred Quarter Method, are often used as an alternative. In this study we investigate the accuracy of different plotless sampling methods. To this end, tree densities of a mangrove forest were determined and compared with estimates provided by several plotless methods. None of these methods proved accurate across all field sites with mean underestimations up to 97% and mean overestimations up to 53% in the field. Applying the methods to different vegetation patterns shows that when random spatial distributions were used the true density was included within the 95% confidence limits of all the plotless methods tested. It was also found that, besides aggregation and regularity, density trends often found in mangroves contribute to the unreliability. This outcome raises questions about the use of plotless sampling in forest monitoring and management, as well as for estimates of density-based carbon sequestration. We give recommendations to minimize errors in vegetation surveys and recommendations for further in-depth research.