Hagihara Rie, Jones Rhondda E, Sobtzick Susan, Cleguer Christophe, Garrigue Claire, Marsh Helene
College of Science and Engineering, James Cook University, Townsville, Queensland, Australia.
Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER), James Cook University, Townsville, Queensland, Australia.
PLoS One. 2018 Jan 25;13(1):e0191476. doi: 10.1371/journal.pone.0191476. eCollection 2018.
The probability of an aquatic animal being available for detection is typically <1. Accounting for covariates that reduce the probability of detection is important for obtaining robust estimates of the population abundance and determining its status and trends. The dugong (Dugong dugon) is a bottom-feeding marine mammal and a seagrass community specialist. We hypothesized that the probability of a dugong being available for detection is dependent on water depth and that dugongs spend more time underwater in deep-water seagrass habitats than in shallow-water seagrass habitats. We tested this hypothesis by quantifying the depth use of 28 wild dugongs fitted with GPS satellite transmitters and time-depth recorders (TDRs) at three sites with distinct seagrass depth distributions: 1) open waters supporting extensive seagrass meadows to 40 m deep (Torres Strait, 6 dugongs, 2015); 2) a protected bay (average water depth 6.8 m) with extensive shallow seagrass beds (Moreton Bay, 13 dugongs, 2011 and 2012); and 3) a mixture of lagoon, coral and seagrass habitats to 60 m deep (New Caledonia, 9 dugongs, 2013). The fitted instruments were used to measure the times the dugongs spent in the experimentally determined detection zones under various environmental conditions. The estimated probability of detection was applied to aerial survey data previously collected at each location. In general, dugongs were least available for detection in Torres Strait, and the population estimates increased 6-7 fold using depth-specific availability correction factors compared with earlier estimates that assumed homogeneous detection probability across water depth and location. Detection probabilities were higher in Moreton Bay and New Caledonia than Torres Strait because the water transparency in these two locations was much greater than in Torres Strait and the effect of correcting for depth-specific detection probability much less. The methodology has application to visual survey of coastal megafauna including surveys using Unmanned Aerial Vehicles.
水生动物被检测到的概率通常小于1。考虑那些会降低检测概率的协变量对于获得可靠的种群丰度估计以及确定其现状和趋势至关重要。儒艮（Dugong dugon）是一种以海底为食的海洋哺乳动物，也是海草群落专家。我们假设儒艮被检测到的概率取决于水深，并且儒艮在深水海草栖息地水下停留的时间比在浅水海草栖息地更长。我们通过量化28头佩戴了GPS卫星发射器和时间深度记录器（TDR）的野生儒艮在三个具有不同海草深度分布的地点的深度利用情况来检验这一假设：1）支持延伸至40米深的广阔海草草甸的开阔水域（托雷斯海峡，6头儒艮，2015年）；2）一个有广阔浅海草床的受保护海湾（平均水深6.8米）（摩顿湾，13头儒艮，2011年和2012年）；3）一个深度达60米的泻湖、珊瑚和海草栖息地混合区域（新喀里多尼亚，9头儒艮，2013年）。所安装的仪器用于测量儒艮在各种环境条件下在实验确定的检测区域内停留的时间。将估计的检测概率应用于之前在每个地点收集的航空调查数据。总体而言，儒艮在托雷斯海峡被检测到的可能性最小，与之前假设整个水深和地点检测概率均一的估计相比，使用特定深度的可检测性校正因子后，种群估计数增加了6至7倍。摩顿湾和新喀里多尼亚的检测概率高于托雷斯海峡，因为这两个地点的水体透明度远高于托雷斯海峡，且校正特定深度检测概率的影响要小得多。该方法适用于沿海大型动物的视觉调查，包括使用无人机进行的调查。
