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Refining, testing and evaluating spatially explicit models for wind dispersed plants.

机译:完善,测试和评估风散发植物的空间显式模型。

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Dispersal is the process by which plants expand their range and explore new habitats. When local habitats become inhospitable, dispersal ability becomes the key mechanism allowing species to evade extinction. Despite the efforts in obtaining empirical dispersal curves and developing sophisticated spatial models, the main issue that remains unresolved is that of scale. Although predictions at the local scale are better than those aiming to describe dispersal at greater distances, they remain too unrealistic to be used in subsequent models that govern growth, mortality and resource exploitation. My first chapter aims to improve predictions at the local scale by refining the parameters of a spatially explicit model. I determined the effect of substituting basal area for cone production as a proxy for seed output. The results showed that the r2 from the regression of predicted versus observed densities increased by 5% for seeds and 15% for seedling simulations. Next, I determined the effects of allowing the horizontal wind speeds to vary. The results showed that correlations of observed vs. predicted recruitment are a function of the assumed meteorological conditions used to drive them. My second chapter tested the ability of inverse modeling to predict recruitment both at the stand level and beyond. Using the maternally derived DNA from seed coats of the North American tree species Pinus strobus, I compared the most common approach (inverse modeling) with the newer but far more time-consuming method of using microsatellite markers. I showed that inverse modeling grossly underestimates seed dispersal potential in this species and thus caution against its continued use. With the aim to improve spatial models, this thesis would not be complete without an examination of the role of wind on seed abscission---the precursor to dispersal. Previous attempts to link the probability of abscission with meteorological phenomena were set within averaging times that exceeded the time frame of seed abscission (1 second) by at least 15 times. Using 1-minute averaging times, I showed that seed release, for the wind dispersed tropical tree Ceiba aesculifolia, is proportional to the square of the horizontal wind speeds. Furthermore, the data showed that this relationship is highly time sensitive where a correlation is no longer evident at averaging times exceeding 25-minute intervals. This thesis is concluded by showing that updrafts are much more effective at causing seed release than all other wind directions (i.e. downdrafts and horizontal). What it can not show, however, is the frequency of upward abscission events within a forest environment and how these results can be implemented within spatially explicit models that predict the dispersal potential of seeds traveling horizontally or vertically. Indeed, this can be addressed in future work.
机译:分散是植物扩大其范围并探索新栖息地的过程。当当地栖息地变得荒凉时,扩散能力成为使物种逃避灭绝的关键机制。尽管在获取经验弥散曲线和开发复杂的空间模型方面付出了很多努力,但仍未解决的主要问题是规模问题。尽管在本地尺度上的预测要比旨在描述更远距离扩散的预测更好,但它们仍然过于不切实际,因此无法用于控制增长,死亡率和资源开发的后续模型中。我的第一章旨在通过优化空间显式模型的参数来改进局部尺度的预测。我确定了用基部面积代替圆锥生产作为种子产量替代的效果。结果表明,从预测的密度与观察到的密度的回归来看,r2对种子增加了5%,对于幼苗模拟增加了15%。接下来,我确定了允许水平风速变化的效果。结果表明,观测到的和预计的补充之间的相关性是用来驱动它们的假定气象条件的函数。我的第二章测试了逆向建模的能力,以预测展位范围内及以后的招聘情况。我使用了北美树种Pinus strobus种皮的母本衍生DNA,将最常见的方法(逆建模)与使用微卫星标记的更新但耗时得多的方法进行了比较。我发现逆向模型严重低估了该物种的种子传播潜力,因此请注意不要继续使用它。为了改善空间模型,如果不研究风对种子脱落的影响,种子脱落是种子传播的先驱,这篇论文是不完整的。先前将脱落的可能性与气象现象联系起来的尝试是在平均时间范围内进行的,该平均时间超过种子脱落的时间范围(<1秒)至少15倍。使用1分钟的平均时间,我发现对于风散布的热带树木木棉,种子的释放与水平风速的平方成正比。此外,数据表明这种关系是高度时间敏感的,其中在超过25分钟间隔的平均时间不再具有相关性。本论文的结论是,表明向上气流在引起种子释放方面比所有其他风向(即向下气流和水平风向)更有效。但是,无法显示的是森林环境中发生的向上脱落事件的频率,以及如何在预测水平或垂直传播的种子传播潜力的空间显式模型中实现这些结果。确实,这可以在将来的工作中解决。

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