Department of Integrative Biology, Michigan State University, East Lansing, Michigan, 48824, USA.
Ecology, Evolutionary Biology, and Behavior Program, Michigan State University, East Lansing, Michigan, 48824, USA.
Ecology. 2017 Jun;98(6):1640-1650. doi: 10.1002/ecy.1831. Epub 2017 May 11.
There is increasing need for methods that integrate multiple data types into a single analytical framework as the spatial and temporal scale of ecological research expands. Current work on this topic primarily focuses on combining capture-recapture data from marked individuals with other data types into integrated population models. Yet, studies of species distributions and trends often rely on data from unmarked individuals across broad scales where local abundance and environmental variables may vary. We present a modeling framework for integrating detection-nondetection and count data into a single analysis to estimate population dynamics, abundance, and individual detection probabilities during sampling. Our dynamic population model assumes that site-specific abundance can change over time according to survival of individuals and gains through reproduction and immigration. The observation process for each data type is modeled by assuming that every individual present at a site has an equal probability of being detected during sampling processes. We examine our modeling approach through a series of simulations illustrating the relative value of count vs. detection-nondetection data under a variety of parameter values and survey configurations. We also provide an empirical example of the model by combining long-term detection-nondetection data (1995-2014) with newly collected count data (2015-2016) from a growing population of Barred Owl (Strix varia) in the Pacific Northwest to examine the factors influencing population abundance over time. Our model provides a foundation for incorporating unmarked data within a single framework, even in cases where sampling processes yield different detection probabilities. This approach will be useful for survey design and to researchers interested in incorporating historical or citizen science data into analyses focused on understanding how demographic rates drive population abundance.
随着生态研究的空间和时间尺度的扩大，越来越需要将多种数据类型整合到一个单一的分析框架中。目前，关于这个主题的工作主要集中在将标记个体的捕获-再捕获数据与其他数据类型结合到综合种群模型中。然而，物种分布和趋势的研究通常依赖于广泛范围内未标记个体的数据，而在这些范围内，局部丰度和环境变量可能会有所不同。我们提出了一种将检测-未检测和计数数据整合到单个分析中的建模框架，以估计种群动态、丰度和个体在采样过程中的检测概率。我们的动态种群模型假设，根据个体的存活和繁殖及迁入带来的增长，特定地点的个体数量可以随时间而变化。我们通过假设在采样过程中，每个存在于一个地点的个体都有相同的被检测到的概率，来对每个数据类型的观测过程进行建模。我们通过一系列模拟来检验我们的建模方法，这些模拟说明了在各种参数值和调查配置下，计数与检测-未检测数据的相对价值。我们还通过将西北太平洋地区 barred owl（Strix varia）的长期检测-未检测数据（1995-2014 年）与新收集的计数数据（2015-2016 年）结合，提供了一个模型的实证示例，以研究影响种群数量随时间变化的因素。我们的模型为在单个框架内纳入未标记数据提供了基础，即使在采样过程产生不同检测概率的情况下也是如此。这种方法将对调查设计和研究人员有用，他们有兴趣将历史或公民科学数据纳入分析中，以了解人口增长率如何驱动种群数量。
