Data from: Comparing convenience and probability sampling for urban ecology applications

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.

1. 城市森林生态系统可提供多种生态系统服务（ecosystem services），因此需从群落结构与组成维度量化其生态特征，以便在生态系统服务模型中更精准地阐释其生态效益。高效的采样与监测方法在此过程中至关重要。
2. 受时间与经费限制，全树木清查工作较为稀缺，因此催生了多种采样方法。当前植被调查愈发倾向于采用基于样地的分层随机采样（stratified-random plot-based sampling），以降低便利抽样（convenience sampling）带来的偏差——尽管便利抽样可节省时间并获得更高的物种丰富度得分（species richness scores）。城市景观普遍存在高强度同种聚集现象且物种多样性丰富，为对比这两种采样方法提供了独特的生物地理学研究场景。
3. 本研究选取意大利梅拉诺（Meran）市城市森林的两处空间覆盖范围较广的便利采样样本，并与200个随机圆形样地（random circular plots）的采样结果进行对比，分析二者的群落结构、树木特征及生态系统服务供给情况。
4. 便利抽样获得了更高的物种多样性，纳入了更多稀有种，这是因为其单位采样时间内覆盖的区域更广。将伪随机亚样地（pseudorandom sub-plots）与随机圆形样地对比后发现，二者的香农多样性指数（Shannon diversity）与采样可比性指数（sampling comparability indices）并无显著差异。两种采样方法测得的树木变量（胸径（diameter at breast height）、树高、树冠宽度、枝下高（height to crown base））以及生态系统服务模型输出结果均较为相似。
5. 综合与应用：研究结果表明，只要在采样设计中纳入土地利用类型分层策略（stratification by land-use type），便利抽样可作为随机抽样的替代方案，节省时间与经费成本。更高的物种丰富度可潜在提升城市生态模型的预测精度——这类模型依赖于物种特异性功能性状（species-specific functional traits）。