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Methods for Measuring Bird-Mediated Seed Rain: Insights from a Hawaiian Mesic Forest

机译:测量鸟类晶雨的方法:夏威夷浅林洞察力

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摘要

Amount and diversity of bird-dispersed seed rain play important roles in determining forest composition, yet neither is easy to quantify. The complex ecological processes that influence seed movement make the best approach -highly context specific. Although recent advances in seed rain theory emphasize quantifying source-specific seed shadows, many ecological questions can be -addressed u sing a less mechanistic approach that requires fewer assumptions. Using seed rain rates from 0.38 m(2) hoop traps sampled twice monthly over the course of a year, we show that number of traps required to identify changes in seed rain varies across seed species and forest type. Detecting a 50% increase in amount of seed rain required from 65 to > 300 traps, while detecting a 200% increase generally required <= 50 traps. Trap size and ecological context dictate the number of seeds found in each trap, but the coefficient of variation (CV) across traps in a given ecological context can help inform future studies about number of traps needed to detect change. To better understand factors influencing variation around estimates of seed rain, we simulated both clustered and evenly distributed patterns of fecal deposition using three different levels of seed aggregation (number of seeds in each fecal deposit). When patterns of fecal -deposition were clustered, rather than evenly dispersed across the study area, they required > 1.5 times the number of traps to identify a 100% increase in seed rain. Similarly, we found that low seed aggregation required > 1.5 times the number of traps to detect a 100% change than when aggregation was medium or high. At low aggregations, fewer seed rain traps contained seeds (low, 33 +/- 5%; medium, 23 +/- 4%; high, 24 +/- 5%), resulting in more variation across traps than medium and high aggregations. We also illustrate the importance of training -observers to discern between morphologically similar seeds from different species and provide resources to help identify bird-dispersed seeds commonly found within midelevation mesic Hawaiian forests.
机译:鸟类分散的种子雨量的数量和多样性在确定森林组成中的重要作用,但既不易于量化。影响种子运动的复杂生态过程使得最佳方法 - 高表程特异性。虽然近期种子雨理论的进步强调量化源特定的种子阴影,但许多生态问题可以 - 用来唱一种需要更少的假设的机械方法。使用0.38米(2)箍陷阱的种子雨率在一年内每月取样两次,我们展示了识别种子雨变化所需的陷阱数量在种子种类和森林类型中变化。检测65〜> 300陷阱所需的种子雨量的量增加50%,同时检测到200%的增加,通常需要<= 50陷阱。陷阱大小和生态背景决定了每个陷阱中发现的种子数量,但在给定的生态背景下陷阱的变化系数(CV)可以帮助通知未来关于检测变化所需的陷阱数量的研究。为了更好地了解影响种子雨估计变异的因素,我们使用三种不同水平的种子聚集(每种粪便沉积物中的种子数)模拟群体沉积的聚类和均匀分布的模式。当粪便模式的聚集成簇时,而不是均匀地分散在研究区域,它们需要> 1.5倍的陷阱数量以识别种子雨量增加100%。类似地,我们发现所需的低种子聚集>检测到陷阱的陷阱数量的1.5倍,而不是聚集在中等或高的聚集时。在低聚集中,含有种子的种子(低,33 +/- 5%;中等,23 +/- 4%;高,24 +/- 5%),导致陷阱比中等和高聚集更多的变化。我们还说明了训练 - 多元化物在不同物种的形态上类似种子之间辨别的重要性,并提供资源,以帮助识别常见于中间人中的鸟类分散的种子。

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