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首页> 美国卫生研究院文献>Springer Open Choice >Drive counts as a method of estimating ungulate density in forests: mission impossible?
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Drive counts as a method of estimating ungulate density in forests: mission impossible?

机译：驱动计数是估计森林中有蹄类动物密度的一种方法：不可能完成任务吗？

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Although drive counts are frequently used to estimate the size of deer populations in forests, little is known about how counting methods or the density and social organization of the deer species concerned influence the accuracy of the estimates obtained, and hence their suitability for informing management decisions. As these issues cannot readily be examined for real populations, we conducted a series of ‘virtual experiments’ in a computer simulation model to evaluate the effects of block size, proportion of forest counted, deer density, social aggregation and spatial auto-correlation on the accuracy of drive counts. Simulated populations of red and roe deer were generated on the basis of drive count data obtained from Polish commercial forests. For both deer species, count accuracy increased with increasing density, and decreased as the degree of aggregation, either demographic or spatial, within the population increased. However, the effect of density on accuracy was substantially greater than the effect of aggregation. Although improvements in accuracy could be made by reducing the size of counting blocks for low-density, aggregated populations, these were limited. Increasing the proportion of the forest counted led to greater improvements in accuracy, but the gains were limited compared with the increase in effort required. If it is necessary to estimate the deer population with a high degree of accuracy (e.g. within 10% of the true value), drive counts are likely to be inadequate whatever the deer density. However, if a lower level of accuracy (within 20% or more) is acceptable, our study suggests that at higher deer densities (more than ca. five to seven deer/100 ha) drive counts can provide reliable information on population size.
机译：尽管经常使用驱动器计数来估计森林中鹿种群的数量，但对于有关的鹿种的计数方法或密度和社会组织如何影响所获得的估计的准确性，以及因此它们是否适合指导管理决策的了解很少。 。由于这些问题无法轻易地通过实际人口进行检验，因此我们在计算机仿真模型中进行了一系列“虚拟实验”，以评估区块大小，森林计数比例，鹿密度，社会聚集度和空间自相关性对森林种群的影响。驱动器计数的准确性。根据从波兰商品林获得的驱动器计数数据生成了模拟的马鹿和ro种群。对于这两种鹿，计数精度都随着密度的增加而增加，并且随着种群内人口或空间聚集度的增加而降低。但是，密度对准确性的影响远大于聚集的影响。尽管可以通过减少低密度汇总人口的计数块的大小来提高准确性，但这些是有限的。增加森林数量的比例导致了准确性的更大提高，但是与所需工作量的增加相比，收益是有限的。如果有必要以高准确度（例如，在真实值的10％之内）估算鹿的数量，那么无论鹿的密度如何，驱动器计数都可能不足。但是，如果可接受较低的准确度（20％或更高），我们的研究表明，在较高的鹿密度下（超过大约五到七个鹿/ 100公顷），驱动器计数可以提供有关种群数量的可靠信息。

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