Quantifying range-wide variation in population trends from local abundance surveys and widespread opportunistic occurrence records
Quantifying range-wide variation in population trends from local abundance surveys and widespread opportunistic occurrence records
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DOI：
10.1111/2041-210x.12221
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发表时间：
2014-08-01
影响因子：
6.6
通讯作者：
Schurr, Frank M.
中科院分区：
文献类型：
--
作者：
Pagel, Joern;Anderson, Barbara J.;Schurr, Frank M.
1.物种的丰度在空间和时间上都有差异。描述这些模式是宏观生态学的基石。此外，种群规模的趋势是评估物种保护状况的一个重要标准。由于丰度趋势在空间上是不均匀的，我们需要量化一个物种的地理范围内丰度趋势的变化。一个基本的困难是，覆盖大面积地理区域的数据集很少包括高时间分辨率的人口丰度数据。虽然大规模的地理分布数据和特定地点的人口趋势数据越来越广泛，但需要采用综合这些不同类型数据的方法。我们提出了一个分层模型，整合了多个来源的观测，估计时空丰度趋势。该模型将空间网格上的年度人口密度与长期计数数据和公民科学计划的机会发生记录联系起来。两种数据类型的特定观测模型明确地解释了数据结构和质量的差异。我们测试这种新的方法在一个虚拟的研究与模拟数据，并将其应用到整个范围内的蝴蝶物种（Pyronia tithonus）在英国1985年至2004年的丰度动态估计。模拟和真实的数据的应用演示了分层模型结构如何适应观测数据和模拟丰度之间联系的不同阶段出现的各种不确定性来源，从而解释了丰度变化推断中的这些不确定性.我们表明，通过使用分层观测模型，整合不同类型的常用数据源，我们可以提高跨空间和时间的物种丰度变化的估计。这将提高我们探测区域趋势的能力，也可以加强理解范围动态的经验基础。
1. Species' abundances vary in space and time. Describing these patterns is a cornerstone of macroecology. Moreover, trends in population size are an important criterion for the assessment of a species' conservation status. Because abundance trends are not homogeneous in space, we need to quantify variation in abundance trends across the geographical range of a species. A basic difficulty exists in that data sets that cover large geographic areas rarely include population abundance data at high temporal resolution. Whilst both broad-scale geographic distribution data and site-specific population trend data are becoming more widely available, approaches are required which integrate these different types of data.2. We present a hierarchical model that integrates observations from multiple sources to estimate spatio-temporal abundance trends. The model links annual population densities on a spatial grid to both long-term count data and to opportunistic occurrence records from a citizen science programme. Specific observation models for both data types explicitly account for differences in data structure and quality.3. We test this novel method in a virtual study with simulated data and apply it to the estimation of abundance dynamics across the range of a butterfly species (Pyronia tithonus) in Great Britain between 1985 and 2004. The application to simulated and real data demonstrates how the hierarchical model structure accommodates various sources of uncertainty which occur at different stages of the link between observational data and the modelled abundance, thereby it accounts for these uncertainties in the inference of abundance variations.4. We show that by using hierarchical observation models that integrate different types of commonly available data sources, we can improve the estimates of variation in species abundances across space and time. This will improve our ability to detect regional trends and can also enhance the empirical basis for understanding range dynamics.