Global patterns and predictors of avian population density

IF 6.3 1区 环境科学与生态学 Q1 ECOLOGY
Luca Santini, Joseph A. Tobias, Corey Callaghan, Juan Gallego-Zamorano, Ana Benítez-López
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引用次数: 1

Abstract

Aim

How population density varies across animal species in the context of environmental gradients, and associated migratory strategies, remains poorly understood. The recent influx of avian trait data and population density estimates allows these patterns to be described and explored in unprecedented detail. This study aims to identify the main macroecological drivers of population density in birds.

Location

Global.

Time period

1970–2021.

Major taxa studied

Birds (Aves).

Methods

We collated a dataset of 5072 local population density estimates for 1853 species and modelled population density as a function of trait and environmental predictors in a Bayesian framework accounting for phylogenetic and spatial autocorrelation. We explored the influence of body mass, diet, primary lifestyle, mating system, nesting behaviour, territoriality, and migratory behaviour on population density, accounting for a range of environmental variables, including preferred habitat type, primary productivity, precipitation and temperature. Based on this empirical baseline, we then predicted the mean population density for 9089 species of birds and estimated global geographic patterns of bird population density.

Results

Population density was lower in species with larger body mass and higher trophic levels, and also declined in territorial species, migratory species, brood parasites and species inhabiting resource-poor habitat types (e.g., deserts). Conversely, population density increased in cooperative breeders. Environmental drivers were most influential for migratory birds, with precipitation and temperature both associated with higher population density. Overall, bird population densities were higher at lower latitudes.

Main conclusions

Our results support previous findings on the role of body mass, diet and environmental gradients, but also reveal novel species-specific drivers of avian densities related to reproduction, migration and resource-holding behaviour. Substantial fine-scale variation remains unexplained. We provide a global dataset of population density predictions for use in macroecological analyses and conservation assessments.

全球鸟类种群密度格局和预测因子
目的动物种群密度在环境梯度背景下的变化,以及相关的迁徙策略,仍然知之甚少。最近大量的鸟类特征数据和种群密度估计使得这些模式能够以前所未有的细节进行描述和探索。本研究旨在确定鸟类种群密度的主要宏观生态驱动因素。位置 全球。时间段1970-2021。主要分类群为鸟类(鸟)。方法整理了1853个物种的5072个种群密度估算数据集,并在考虑系统发育和空间自相关的贝叶斯框架下,将种群密度作为性状和环境预测因子的函数进行建模。我们探讨了体重、饮食、主要生活方式、交配系统、筑巢行为、领土和迁徙行为对种群密度的影响,并考虑了一系列环境变量,包括首选栖息地类型、初级生产力、降水和温度。在此基础上,我们预测了9089种鸟类的平均种群密度,并估算了全球鸟类种群密度的地理格局。结果大体重、高营养等级的物种种群密度较低,领地物种、迁徙物种、寄主物种和荒漠等资源贫乏生境类型的物种种群密度均呈下降趋势。相反,群体密度在合作育种者中增加。环境因素对候鸟的影响最大,降水和温度都与较高的种群密度有关。总体而言,低纬度地区鸟类种群密度较高。我们的研究结果支持了先前关于体重、饮食和环境梯度的作用的发现,但也揭示了与繁殖、迁徙和资源持有行为有关的鸟类密度的新物种特异性驱动因素。大量的精细尺度变化仍然无法解释。我们提供了一个全球人口密度预测数据集,用于宏观生态分析和保护评估。
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来源期刊
Global Ecology and Biogeography
Global Ecology and Biogeography 环境科学-生态学
CiteScore
12.10
自引率
3.10%
发文量
170
审稿时长
3 months
期刊介绍: Global Ecology and Biogeography (GEB) welcomes papers that investigate broad-scale (in space, time and/or taxonomy), general patterns in the organization of ecological systems and assemblages, and the processes that underlie them. In particular, GEB welcomes studies that use macroecological methods, comparative analyses, meta-analyses, reviews, spatial analyses and modelling to arrive at general, conceptual conclusions. Studies in GEB need not be global in spatial extent, but the conclusions and implications of the study must be relevant to ecologists and biogeographers globally, rather than being limited to local areas, or specific taxa. Similarly, GEB is not limited to spatial studies; we are equally interested in the general patterns of nature through time, among taxa (e.g., body sizes, dispersal abilities), through the course of evolution, etc. Further, GEB welcomes papers that investigate general impacts of human activities on ecological systems in accordance with the above criteria.
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