Recent stability of resident and migratory landbird populations in National Parks of the Pacific Northwest
Recent stability of resident and migratory landbird populations in National Parks of the Pacific Northwest
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太平洋西北地区国家公园常驻和迁徙陆地鸟类种群的近期稳定性
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
M. H. Huff
中科院分区：
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作者：
C. Ray;J. Saracco;Mandy L Holmgren;Robert L. Wilkerson;R. Siegel;K. Jenkins;J. Ransom;P. J. Happe;J. Boetsch;M. H. Huff
监测国家公园的物种有助于推断气候变化对种群动态的影响，因为公园相对不受其他形式的人为干扰的影响。即使在监测计划的早期，识别人口密度的气候协变量也可能暗示未来变化的脆弱性。在繁殖季节监测公园中的土鸟种群带来了额外的好处，即允许对具有不同需求的一大批物种进行比较分析。例如，比较在非公园栖息地暴露的不同栖息地的栖息和迁徙物种可以揭示公园效应的相对重要性，例如与当地气候有关的影响。我们使用2005-2014年间在太平洋西北部三个荒野地区(雷尼尔山、北喀斯喀兹和奥林匹克国家公园)收集的繁殖季点计数数据来监测地鸟。对于39个物种，我们使用贝叶斯分层N-混合模型估计了种群密度的最新趋势，同时考虑了个体的检测概率。我们的分析综合了N混合模型中的几个最新发展，包括区间和距离抽样来估计检测概率的不同分量，同时也考虑了不同持续时间的计数间隔、点计数样带的长度和数量的年度变化、空间自相关性、随机效应以及检测和密度的协变量。作为密度的协变量，我们考虑了假设的影响繁殖成功的降水和温度的指标。我们还考虑了公园和海拔地层对趋势的影响。不管模型结构如何，我们估计了2005-2014年间大多数人口的密度稳定或增加。不同物种的平均趋势对每个公园的候鸟和一个公园的居民都是积极的。该地区最近的降雪量不足可能促成了这一积极趋势，因为人口密度与移民和居民的降雨量成反比。密度与春季平均温度成正比，但幅度小得多。我们的方法体现了一个分析框架，用于根据点数数据估计趋势，并评估气候和其他时空变量在推动这些趋势方面的作用。了解人口趋势及其驱动因素，对于气候变化背景下的适应性管理和资源管理至关重要。
Monitoring species in National Parks facilitates inference regarding effects of climate change on population dynamics because parks are relatively unaffected by other forms of anthropogenic disturbance. Even at early points in a monitoring program, identifying climate covariates of population density can suggest vulnerabilities to future change. Monitoring landbird populations in parks during the breeding season brings the added benefit of allowing a comparative approach to inference across a large suite of species with diverse requirements. For example, comparing resident and migratory species that vary in exposure to non-park habitats can reveal the relative importance of park effects, such as those related to local climate. We monitored landbirds using breeding-season point-count data collected during 2005–2014 in three wilderness areas of the Pacific Northwest (Mount Rainier, North Cascades, and Olympic National Parks). For 39 species, we estimated recent trends in population density while accounting for individual detection probability using Bayesian hierarchical N-mixture models. Our analyses integrated several recent developments in N-mixture modeling, incorporating interval and distance sampling to estimate distinct components of detection probability while also accommodating count intervals of varying duration, annual variation in the length and number of point-count transects, spatial autocorrelation, random effects, and covariates of detection and density. As covariates of density, we considered metrics of precipitation and temperature hypothesized to affect breeding success. We also considered effects of park and elevational stratum on trend. Regardless of model structure, we estimated stable or increasing densities during 2005–2014 for most populations. Mean trends across species were positive for migrants in every park and for residents in one park. A recent snowfall deficit in this region might have contributed to the positive trend, because population density varied inversely with precipitation-as-snow for both migrants and residents. Densities varied directly but much more weakly with mean spring temperature. Our approach exemplifies an analytical framework for estimating trends from point-count data, and for assessing the role of climatic and other spatiotemporal variables in driving those trends. Understanding population trends and the factors that drive them is critical for adaptive management and resource stewardship in the context of climate change.