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Population models for social species: lessons learned from models of Red-cockaded Woodpeckers (Picoides borealis)

机译:社会物种的种群模型:从红冠啄木鸟模型中获得的经验教训

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Behavior can have major impacts on the population dynamics of social species and should be incorporated into demographic models to realistically evaluate population trends and extinction risk. We compared the predictions of a stage- and age-based matrix model, an individual-based model (IBM, developed in the program Vortex), and a spatially explicit individual-based model (SEPM) with the actual dynamics of a population of Red-cockaded Woodpeckers (RCW; Picoides borealis) in the Sandhills of North Carolina, USA. Predictions, including population size, composition, and growth rate, differed the most from actual population characteristics for models that did not incorporate social structure. The SEPM most closely predicted actual population dynamics, underestimating the population by 2.3%. This model, specifically developed to simulate RCW population dynamics, contains many of the features that we assert are important for adequately incorporating social behavior into demographic and population modeling. These features include the ability to (1) differentiate individuals based on their stage class, (2) capture the dynamics of the population at both the individual and group level, (3) incorporate the positive or negative effects of subdominants, (4) include environmental and demographic stochasticity, and (5) capture dispersal and other spatial factors. The RCW SEPM, although currently species-specific, provides a strong blueprint for how population models for social species could be constructed in the future when data allow.
机译:行为可能会对社会物种的种群动态产生重大影响,应将其纳入人口模型,以切实评估种群趋势和灭绝风险。我们将基于阶段和年龄的矩阵模型,基于个人的模型(IBM,在Vortex程序中开发)和基于空间显式的基于个体的模型(SEPM)的预测与红色种群的实际动态进行了比较。在美国北卡罗莱纳州的沙丘上的双冠啄木鸟(RCW; Picoidesborealis)。对于未包含社会结构的模型,包括人口规模,构成和增长率在内的预测与实际人口特征的差异最大。 SEPM最精确地预测了实际人口动态,低估了2.3%。该模型是专门为模拟RCW人口动态而开发的,包含许多我们认为对于将社会行为充分纳入人口统计和人口模型中至关重要的功能。这些功能包括(1)根据阶段类别区分个体的能力,(2)在个体和群体水平上捕获人口动态,(3)纳入次要优势的正面或负面影响,(4)包括环境和人口的随机性,以及(5)捕获分散性和其他空间因素。 RCW SEPM尽管目前是特定于物种的,但为在数据允许的情况下将来如何构建社会物种的种群模型提供了强有力的蓝图。

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