Seed dispersal has key implications for community dynamics and restoration ecology. However, estimating seed rain (the number and diversity of seeds arriving in a given area) is challenging, and the lack of standardization in measurement prevents cross-site comparisons. Seed trap effectiveness and accuracy of seed sorting methods are key components of seed rain estimates in need of standardization. We propose and describe a standardized protocol for evaluating the effectiveness of two seed trap types (sticky and funnel traps) and the accuracy of a seed sorting method. We used widely available seeds (arugula, quinoa, sesame and sunflower) to produce a gradient of seed size, weight and colour. Proof-of-concept was tested in a tropical grassland, where traps were set for 30 days. Our results suggest that we underestimate dispersal of seeds with less than 2 mm width that can be easily mistaken for debris and soil particles or that fail to adhere to sticky traps. Seeds on sticky traps may be more vulnerable to removal by wind and rain, whereas seeds in funnel traps are more susceptible to decay. We found no evidence of observer bias on seed sorting for funnel trap samples. However, accuracy on seed sorting for funnel trap samples tended to decline for seeds with less than 2 mm width, suggesting a size-dependence in seed retrieval success. Our standardized protocol addressing trap effectiveness and seed sorting methods will increase the reliability of data obtained in seed rain studies and allow more reliable comparisons between datasets.

中文翻译：

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一个简单的标准化协议来评估种子雨估计的可靠性

种子传播对群落动态和恢复生态具有重要意义。然而，估算种子雨（到达给定区域的种子的数量和多样性）具有挑战性，并且缺乏标准化的测量阻碍了跨站点比较。种子捕集器的有效性和种子分选方法的准确性是需要标准化的种子雨估算的关键组成部分。我们提出并描述了一种标准化协议，用于评估两种种子陷阱类型（粘性和漏斗陷阱）的有效性和种子分选方法的准确性。我们使用广泛可用的种子（芝麻菜、藜麦、芝麻和向日葵）来产生种子大小、重量和颜色的梯度。概念验证在热带草原上进行了测试，陷阱设置了 30 天。我们的研究结果表明，我们低估了宽度小于 2 毫米的种子的传播，这些种子很容易被误认为是碎片和土壤颗粒，或者无法附着在粘性陷阱上。粘性陷阱上的种子可能更容易被风雨带走，而漏斗陷阱中的种子更容易腐烂。我们没有发现任何证据表明观察者对漏斗陷阱样本的种子分选存在偏见。然而，对于宽度小于 2 毫米的种子，漏斗陷阱样本的种子分选准确性往往会下降，这表明种子检索成功与大小有关。我们解决陷阱有效性和种子分类方法的标准化协议将提高在种子雨研究中获得的数据的可靠性，并允许在数据集之间进行更可靠的比较。