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Effects of environmental synchrony and density-dependent dispersal on temporal and spatial slopes of Taylor's law

机译:环境同步和密度依赖性分散对泰勒法的时间和空间斜坡的影响

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Taylor's law (TL) is an empirical rule that describes an approximate relationship between the variance and mean of population density: log(10)(variance) approximate to log(10)(a) + b x log(10)(mean). Population synchrony is another prevailing feature observed in empirical populations. This study investigated the effects of environmental synchrony and density-dependent dispersal on the temporal (b( T)) and spatial (b( S)) slopes of TL, using an empirical dataset of gray-sided vole populations and simulation analyses based on the second-order autoregressive (AR) model. Eighty-five empirical populations satisfied the temporal and spatial TLs with b( T) = 1.943 (+/- SE 0.143) and b( S) = 1.579 (+/- SE 0.136). The pairwise synchrony of population was 0.377 +/- 0.199 (mean +/- SD). Most simulated populations that obeyed the AR model satisfied the form of the temporal and spatial TLs without being affected by the environmental synchrony and density-dependent dispersal; however, those simulated slopes were too steep. The incorporation of environmental synchrony resulted in reduced simulated slopes, but those slopes, too, were still unrealistically steep. By incorporating density-dependent dispersal, simulated slopes decreased and fell within a realistic range. However, the simulated populations without environmental synchrony did not exhibit an adequate degree of density synchrony. In simulations that included both environmental synchrony and density-dependent dispersal, 92.7% of the simulated datasets provided realistic values for b( T), b( S) and population synchrony. Because the two slopes were more sensitive to the variation of density-dependent dispersal than that of environmental synchrony, density-dependent dispersal may be the key to the determination of b( T) and b( S).
机译:泰勒的法律(TL)是一个经验规则,描述了人口密度方差和平均值之间的近似关系:log(10)(方差)近似到log(10)(a)+ b x log(10)(均值)。人口同步是在​​经验群体中观察到的另一个普遍特征。本研究调查了环境同步和密度依赖性分散在TL的时间(B(T))和空间(B(S))斜坡上的影响,使用灰度渣群的经验数据集和基于灰度群群和模拟分析二阶自回归(AR)模型。八十五个经验群体满足于B(T)= 1.943(+/- SE 0.143)和B(S)= 1.579(+/- SE 0.136)的时间和空间TL。一对人口同步为0.377 +/- 0.199(平均+/- SD)。遵守AR模型的大多数模拟群体满足时间和空间TLS的形式,而不受环境同步和密度依赖性分散的影响;然而,那些模拟的斜坡太陡峭。环境同步融合的融合导致模拟斜坡减少,但这些斜坡也仍然不切实际上陡峭。通过纳入密度依赖的分散,模拟斜率降低并落入现实范围内。然而,没有环境同步的模拟人群没有表现出足够的密度同步程度。在包括环境同步和密度相关的分散的模拟中,92.7%的模拟数据集提供了B(T),B(S)和人口同步的现实值。因为两个斜率对密度依赖性分散的变化比环境同步的变化更敏感,所以密度依赖性分散可以是测定B(T)和B(S)的关键。

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