Falaschi Mattia, Gibertini Chiara, Lo Parrino Elia, Muraro Martina, Barzaghi Benedetta, Manenti Raoul, Ficetola Gentile Francesco
Department of Environmental Science and Policy, University of Milan, Via Celoria 10, 20133 Milan, Italy.
Laboratoire d'Écologie Alpine, University Grenoble Alpes, University Savoie Mont Blanc, CNRS, LECA, F-38000 Grenoble, France.
Animals (Basel). 2022 Aug 15;12(16):2085. doi: 10.3390/ani12162085.
Most animal species are detected imperfectly and overlooking individuals can result in a biased inference of the abundance patterns and underlying processes. Several techniques can incorporate the imperfect detection process for a more accurate estimation of abundance, but most of them require repeated surveys, i.e., more sampling effort compared to single counts. In this study, we used the dependent double-observer approach to estimate the detection probability of the egg clutches of two brown frog species, Rana dalmatina and R. latastei. We then simulated the data of a declining population at different levels of detection probability in order to assess under which conditions the double counts provided better estimates of population trends compared to naïve egg counts, given the detectability of frog clutches. Both species showed a very high detection probability, with average values of 93% for Rana dalmatina and 97% for R. latastei. Simulations showed that not considering imperfect detection reduces the power of detecting population trends if detection probability is low. However, at high detection probability (>80%), ignoring the imperfect detection does not bias the estimates of population trends. This suggests that, for species laying large and easily identifiable egg clutches, a single count can provide useful estimates if surveys are correctly timed.
大多数动物物种的检测并不完美，忽略个体可能会导致对丰度模式和潜在过程的推断出现偏差。有几种技术可以纳入不完美的检测过程，以更准确地估计丰度，但其中大多数需要重复调查，即与单次计数相比需要更多的采样工作。在本研究中，我们使用依赖双观察者方法来估计两种棕蛙（欧洲林蛙和拉氏蛙）卵块的检测概率。然后，我们模拟了不同检测概率水平下数量下降种群的数据，以便评估在何种条件下，考虑到蛙卵块的可检测性，双次计数与简单的卵计数相比能更好地估计种群趋势。两种蛙都显示出非常高的检测概率，欧洲林蛙的平均值为93%，拉氏蛙为97%。模拟结果表明，如果检测概率较低，不考虑不完美检测会降低检测种群趋势的能力。然而，在高检测概率（>80%）时，忽略不完美检测不会使种群趋势估计产生偏差。这表明，对于产下大且易于识别的卵块的物种，如果调查时间正确，单次计数可以提供有用的估计。
