Integrating data from different survey types for population monitoring of an endangered species: the case of the Eld’s deer
Integrating data from different survey types for population monitoring of an endangered species: the case of the Eld’s deer
复制标题
整合不同调查类型的数据以监测濒危物种的种群：坡鹿的案例
作者：
D. Bowler;Erlend B. Nilsen;R. Bischof;R. O’Hara;T. T. Yu;Tun Oo;M. Aung;J. Linnell
尽管它对保护决策很有价值，但我们缺乏大多数物种种群丰度的信息。由于建立大规模监测计划几乎不可行，因此结合多个数据源的统计方法是最大限度地利用现有信息的有前途的方法。我们建立了一个贝叶斯分层模型，结合了缅甸瑞雪都野生动物保护区（SWS）濒危坡鹿的不同调查数据，并在模拟实验中测试了我们的方法。我们将空间限制的线横断面丰度数据与空间更广泛的相机陷阱占用数据相结合，以估计鹿的总丰度。综合模型包括生态模型（两种调查类型共有的，基于 cloglog 转换的发生概率和对数转换的预期丰度之间的等价性）和每种调查类型的单独观测模型。我们估计 SWS 坡鹿的种群规模为 c。 1519（1061-2114），表明它是世界上最大的野生种群。模拟表明，与每次调查的单独分析相比，组合数据的潜在好处包括提高精度和更好地对环境空间变化进行采样。我们的分析方法整合了不同调查方法的优点，在估计物种丰度方面具有广泛的应用，特别是在世界信息匮乏的地区。
Despite its value for conservation decision-making, we lack information on population abundances for most species. Because establishing large-scale monitoring schemes is rarely feasible, statistical methods that combine multiple data sources are promising approaches to maximize use of available information. We built a Bayesian hierarchical model that combined different survey data of the endangered Eld’s deer in Shwesettaw Wildlife Sanctuary (SWS) in Myanmar and tested our approach in simulation experiments. We combined spatially-restricted line-transect abundance data with more spatially-extensive camera-trap occupancy data to enable estimation of the total deer abundance. The integrated model comprised an ecological model (common to both survey types, based on the equivalence between cloglog-transformed occurrence probability and log-transformed expected abundance) and separate observation models for each survey type. We estimated that the population size of Eld’s deer in SWS is c. 1519 (1061–2114), suggesting it is the world’s largest wild population. The simulations indicated that the potential benefits of combining data include increased precision and better sampling of the spatial variation in the environment, compared to separate analysis of each survey. Our analytical approach, which integrates the strengths of different survey methods, has widespread application for estimating species’ abundances, especially in information-poor regions of the world.