Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, Birmensdorf, 8093, Switzerland.
Info Fauna Karch, UniMail, Bâtiment G, Bellevaux 51, Neuchâtel, 2000, Switzerland.
Ecol Appl. 2021 Sep;31(6):e02357. doi: 10.1002/eap.2357. Epub 2021 May 24.
Monitoring programs serve to detect trends in the distribution and abundance of species. To do so, monitoring programs often use static state variables. Dynamic state variables that describe population dynamics might be more valuable because they allow for a mechanistic understanding of the processes that lead to population trends. We fit multistate occupancy models to data from a country-wide multispecies amphibian occupancy monitoring program and estimated occupancy and breeding probabilities. If breeding probabilities are determinants of occupancy dynamics, then they may serve in monitoring programs as state variables that describe dynamic processes. The results showed that breeding probabilities were low and that a large proportion of the populations had to be considered to be non-breeding populations (i.e., populations where adults are present but no breeding occurs). For some species, the majority of populations were non-breeding populations. We found that non-breeding populations have lower persistence probabilities than populations where breeding occurs. Breeding probabilities may thus explain trends in occupancy but they might also explain other ecological phenomena, such as the success of invasive species, which had high breeding probabilities. Signs of breeding, i.e., the presence of eggs and larvae, were often hard to detect. Importantly, non-breeding populations also had low detection probabilities, perhaps because they had lower abundances. We suggest that monitoring programs should invest more in the detection of life history stages indicative of breeding, and also into the detection of non-breeding populations. We conclude that breeding probability should be used as a state variable in monitoring programs because it can lead to deeper insights into the processes driving occupancy dynamics.
监测项目旨在检测物种分布和丰度的趋势。为此,监测项目通常使用静态状态变量。描述种群动态的动态状态变量可能更有价值,因为它们可以帮助我们理解导致种群趋势的机制。我们使用多状态占有模型来分析全国范围的多物种两栖动物占有监测项目的数据,并估计占有和繁殖概率。如果繁殖概率是占有动态的决定因素,那么它们可能作为描述动态过程的状态变量在监测项目中发挥作用。结果表明,繁殖概率较低,很大一部分种群被认为是非繁殖种群(即成年个体存在但没有繁殖发生的种群)。对于某些物种,大多数种群是非繁殖种群。我们发现,非繁殖种群的持久性概率低于繁殖种群。因此,繁殖概率可以解释占有变化的趋势,但也可以解释其他生态现象,例如繁殖概率较高的入侵物种的成功。繁殖的迹象,即卵和幼虫的存在,往往很难发现。重要的是,非繁殖种群的检测概率也较低,也许是因为它们的丰度较低。我们建议监测项目应更多地投资于检测繁殖相关的生活史阶段,并投资于检测非繁殖种群。我们得出的结论是,繁殖概率应该作为监测项目中的状态变量来使用,因为它可以深入了解导致占有动态的过程。