Trouillier Mario, Meyer Katrin M, Santini Luca, Pe'er Guy
Institute for Botany and Landscape Ecology University Greifswald Greifswald Germany.
Department of Ecosystem Modelling University of Göttingen Göttingen Germany.
Ecol Evol. 2023 Jan 24;13(1):e9752. doi: 10.1002/ece3.9752. eCollection 2023 Jan.
The viability of populations can be quantified with several measures, such as the probability of extinction, the mean time to extinction, or the population size. While conservation management decisions can be based on these measures, it has not yet been explored systematically if different viability measures rank species and scenarios similarly and if one viability measure can be converted into another to compare studies. To address this challenge, we conducted a quantitative comparison of eight viability measures based on the simulated population dynamics of more than 4500 virtual species. We compared (a) the ranking of scenarios based on different viability measures, (b) assessed direct correlations between the measures, and (c) explored if parameters in the simulation models can alter the relationship between pairs of viability measures. We found that viability measures ranked species similarly. Despite this, direct correlations between the different measures were often weak and could not be generalized. This can be explained by the loss of information due to the aggregation of raw data into a single number, the effect of model parameters on the relationship between viability measures, and because distributions, such as the probability of extinction over time, cannot be ranked objectively. Similar scenario rankings by different viability measures show that the choice of the viability metric does in many cases not alter which population is regarded more viable or which management option is the best. However, the more two scenarios or populations differ, the more likely it becomes that different measures produce different rankings. We thus recommend that PVA studies publish raw simulation data, which not only describes all risks and opportunities to the reader but also facilitates meta-analyses of PVA studies.
种群的生存能力可以通过多种指标进行量化，例如灭绝概率、平均灭绝时间或种群规模。虽然保护管理决策可以基于这些指标，但尚未系统地探讨不同的生存能力指标对物种和情景的排名是否相似，以及一种生存能力指标是否可以转换为另一种指标以比较各项研究。为应对这一挑战，我们基于4500多个虚拟物种的模拟种群动态，对八项生存能力指标进行了定量比较。我们比较了：（a）基于不同生存能力指标的情景排名；（b）评估各项指标之间的直接相关性；（c）探究模拟模型中的参数是否会改变成对生存能力指标之间的关系。我们发现，生存能力指标对物种的排名相似。尽管如此，不同指标之间的直接相关性往往较弱，且无法一概而论。这可以通过原始数据汇总为单个数字导致的信息损失、模型参数对生存能力指标之间关系的影响，以及诸如随时间变化的灭绝概率等分布无法客观排名来解释。不同生存能力指标对情景的相似排名表明，在许多情况下，生存能力指标的选择不会改变哪种种群被认为更具生存能力，或者哪种管理方案是最佳的。然而，两种情景或种群的差异越大，不同指标产生不同排名的可能性就越大。因此，我们建议种群生存力分析（PVA）研究公布原始模拟数据，这不仅能向读者描述所有风险和机遇，还便于对PVA研究进行荟萃分析。
