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Risk-based viable population monitoring

机译:基于风险的可行人口监测

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We describe risk-based viable population monitoring, in which the monitoring indicator is a yearly prediction of the probability that, within a given timeframe, the population abundance will decline below a prespecified level. Common abundance-based monitoring strategies usually have low power to detect declines in threatened and endangered species and are largely reactive to declines. Comparisons of the population's estimated risk of decline over time will help determine status in a more defensible manner than current monitoring methods. Monitoring risk is a more proactive approach; critical changes in the population's status are more likely to be demonstrated before a devastating decline than with abundance-based monitoring methods. In this framework, recovery is defined not as a single evaluation of long-term viability but as maintaining low risk of decline for the next several generations. Effects of errors in risk prediction techniques are mitigated through shorter prediction intervals, setting threshold abundances near current abundance, and explicitly incorporating uncertainty in risk estimates. Viable population monitoring also intrinsically adjusts monitoring effort relative to the population's trite status and exhibits considerable robustness to model misspecification. We present simulations showing that risk predictions made with a simple exponential growth model can be effective monitoring indicators for population dynamics ranging from random walk to density dependence with stable, decreasing, or increasing equilibrium. In analyses of time-series data for five species, risk-based monitoring warned of future declines and demonstrated secure status more effectively than statistical tests for trend.
机译:我们描述了基于风险的可行的人口监测,其中监测指标是在给定时间范围内人口数量将下降到预定水平以下的概率的年度预测。常见的基于丰度的监测策略通常无法检测到受威胁和濒临灭绝物种的数量下降,并且对这种下降反应很大。人口估计的随时间下降的风险的比较将有助于以比目前的监测方法更有说服力的方式确定状态。监视风险是一种更主动的方法;与基于丰度的监测方法相比,人口状况的重大变化更有可能在灾难性下降之前得到证明。在此框架中,恢复的定义不是对长期生存能力的单一评估,而是对未来几代人保持较低的下降风险。通过缩短预测间隔,将阈值丰度设置为当前丰度附近,以及明确将不确定性纳入风险估计中,可以减轻风险预测技术中错误的影响。可行的种群监视还可以从本质上调整与种群的陈旧状态相关的监视工作,并具有对模型错误指定进行建模的强大功能。我们提供的模拟结果表明,用简单的指数增长模型进行的风险预测可以有效地监测人口动态,从随机游动到密度依赖性,具有稳定,降低或增加的平衡。在对五个物种的时间序列数据进行分析时,基于风险的监视警告了未来的下降,并且比趋势的统计检验更有效地证明了安全状态。

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