Data from: Comparing convenience and probability sampling for urban ecology applications
Cite this dataset
Speak, Andrew; Escobedo, Francisco J.; Russo, Alessio; Zerbe, Stefan (2019). Data from: Comparing convenience and probability sampling for urban ecology applications [Dataset]. Dryad. https://doi.org/10.5061/dryad.bs1f7c3
1. Urban forest ecosystems confer multiple ecosystem services. There is thus a need to quantify ecological characteristics in terms of community structure and composition so that benefits can be better understood in ecosystem service models. Efficient sampling and monitoring methods are crucial in this process. 2. Full tree inventories are scarce due to time and financial constraints, thus a variety of sampling methods exist. Modern vegetation surveys increasingly use stratified-random plot-based sampling to reduce the bias associated with convenience sampling, even though the latter can save time and increase species richness scores. The urban landscape, with a high degree of conspecific clustering and high species diversity, provides a unique biogeographical case for comparing these two methodological approaches. 3. We use two spatially extensive convenience samples of the urban forest of Meran (Italy) and compare the community structure, tree characteristics and ecosystem service provision with 200 random circular plots. 4. The convenience sampling resulted in a higher species diversity, incorporating more rare species. This is a result of covering more area per unit sampling time. Pseudorandom sub-plots were compared to the random plots revealing similar Shannon diversity and sampling comparability indices. Measured tree variables (diameter at breast height, height, tree-crown width, height to crown base) were similar between the two methods, as were ecosystem service model outputs. 5. Synthesis and applications. The results suggest that convenience sampling may be a time and money saving alternative to random sampling as long as stratification by land-use type is incorporated into the design. The higher species richness can potentially improve the accuracy of urban ecological models, which rely on species-specific functional traits.