Avoiding verisimilitude when modelling ecological responses to climate change: the influence of weather conditions on trapping efficiency in European badgers (Meles meles)
Avoiding verisimilitude when modelling ecological responses to climate change: the influence of weather conditions on trapping efficiency in European badgers (Meles meles)
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DOI：
10.1111/gcb.12942
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发表时间：
2015-10-01
影响因子：
11.6
通讯作者：
Macdonald, David W.
中科院分区：
文献类型：
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作者：
Noonan, Michael J.;Rahman, M. Abidur;Macdonald, David W.
气候变化影响的信号可能是深奥的;因此，对证据的解释必须避免逼真，否则错误的因果关系可能会损害政策决定。研究气候对野生动物种群动态的影响需要捕捉、观察或拍照的能力，并能持续地重新捕获研究个体。在这方面，我们使用19年的数据（1994-2012），详细描述了1179个欧洲獾的生活史超过3288（再）诱捕事件，以测试诱捕效率是否与季节，天气变量（同期和滞后），身体状况指数（BCI）和诱捕效率（TE）。PCA因子负荷表明，TE的影响显着的温度和降水量，以及在这些变量的时间滞后。从多模型推断，BCI是TE的主要驱动力，状态良好的獾不太可能被困。我们的分析表明，这是通过天气变量驱动BCI，影响TE机械地制定的。值得注意的是，非常条件下，穷人的诱捕成功已经与獾的实际生存和人口丰富的好处。使用这些发现进行参数化模拟，预测最佳/最坏情况下的天气条件和BCI导致季节性TE的8.6% +/- 4.9 SD差异，导致最坏情况下可能低估55.0%的种群丰度;最好情况下高估38.6%。有趣的是，模拟显示，虽然任何单一的诱捕会话可能被证明是真实的人口丰度的错误代表，由于天气的影响，延长捕获标记再捕获研究在次优条件下，人口估计的准确性显着下降。我们还使用这些预测方案来探讨天气如何影响英国政府主导的獾诱捕，与结核病管理有关。我们的结论是，人口监测必须校准的可能性，天气条件可能会直接改变陷阱的成功，因此偏置模型设计。
The signal for climate change effects can be abstruse; consequently, interpretations of evidence must avoid verisimilitude, or else misattribution of causality could compromise policy decisions. Examining climatic effects on wild animal population dynamics requires ability to trap, observe or photograph and to recapture study individuals consistently. In this regard, we use 19years of data (1994-2012), detailing the life histories on 1179 individual European badgers over 3288 (re-) trapping events, to test whether trapping efficiency was associated with season, weather variables (both contemporaneous and time lagged), body-condition index (BCI) and trapping efficiency (TE). PCA factor loadings demonstrated that TE was affected significantly by temperature and precipitation, as well as time lags in these variables. From multi-model inference, BCI was the principal driver of TE, where badgers in good condition were less likely to be trapped. Our analyses exposed that this was enacted mechanistically via weather variables driving BCI, affecting TE. Notably, the very conditions that militated for poor trapping success have been associated with actual survival and population abundance benefits in badgers. Using these findings to parameterize simulations, projecting best-/worst-case scenario weather conditions and BCI resulted in 8.6% +/- 4.9 SD difference in seasonal TE, leading to a potential 55.0% population abundance under-estimation under the worst-case scenario; 38.6% over-estimation under the best case. Interestingly, simulations revealed that while any single trapping session might prove misrepresentative of the true population abundance, due to weather effects, prolonging capture-mark-recapture studies under sub-optimal conditions decreased the accuracy of population estimates significantly. We also use these projection scenarios to explore how weather could impact government-led trapping of badgers in the UK, in relation to TB management. We conclude that population monitoring must be calibrated against the likelihood that weather conditions could be altering trap success directly, and therefore biasing model design.