In heterogeneous habitats, individuals sharing a larger part of their home-range are also likely to live in a very similar environment. This ‘common environment’ effect can generate phenotypic similarities between neighbours and lead to the structuring of phenotypes through the habitat. In this study, we used an intensely monitored population of hihi (or stitchbird, Notiomystis cincta ) from New Zealand, to assess whether home-range overlap and genetic relatedness between birds could generate phenotypic resemblance for a wide panel of morphological and life-history traits. Using a multiple-matrix animal model approach to partition the phenotypic variance present in the population, we included a spatial matrix measuring home range overlap between birds and estimated the proportion of variance attributable to space sharing. We detected a clear contribution of space sharing to the overall phenotypic similarity for two traits: hatchling mass and laying date. We also confirmed the very low estimates of genetic heritability already found for this species. These results suggest that models including space sharing can offer further insight into the determinants of individual differences in phenotype. In particular, the spatial matrix helps to capture fine-scale variation of the environment that classic animal models would potentially miss or miss-assign. In this species, results also suggest that small but significant genetic heritability estimates are not upwardly biased by clustering of close relatives in space.

中文翻译：

---

新西兰hihi个体表型空间共享的后果

在异质栖息地中，共享大部分家园的个体也可能生活在非常相似的环境中。这种“共同环境”效应可以在邻居之间产生表型相似性，并导致通过栖息地形成表型。在这项研究中，我们使用了来自新西兰的高度监测的 hihi（或缝合鸟，Notiomystis cincta）种群，来评估鸟类之间的栖息地重叠和遗传相关性是否可以产生广泛的形态学和生活史特征的表型相似性. 使用多矩阵动物模型方法来划分种群中存在的表型方差，我们包括一个空间矩阵，用于测量鸟类之间的家庭范围重叠，并估计归因于空间共享的方差比例。我们检测到空间共享对两个性状的整体表型相似性的明显贡献：孵化质量和产蛋日期。我们还证实了该物种的遗传遗传力估计值非常低。这些结果表明，包括空间共享在内的模型可以进一步了解表型个体差异的决定因素。特别是，空间矩阵有助于捕捉经典动物模型可能会遗漏或错误分配的环境的精细变化。在这个物种中，结果还表明，小而重要的遗传遗传力估计值不会因空间近亲的聚集而产生向上偏差。我们还证实了该物种的遗传遗传力估计值非常低。这些结果表明，包括空间共享在内的模型可以进一步了解表型个体差异的决定因素。特别是，空间矩阵有助于捕捉经典动物模型可能会遗漏或错误分配的环境的精细变化。在这个物种中，结果还表明，小而重要的遗传遗传力估计值不会因空间近亲的聚集而产生向上偏差。我们还证实了该物种的遗传遗传力估计值非常低。这些结果表明，包括空间共享在内的模型可以进一步了解表型个体差异的决定因素。特别是，空间矩阵有助于捕捉经典动物模型可能会遗漏或错误分配的环境的精细变化。在这个物种中，结果还表明，小而重要的遗传遗传力估计值不会因空间近亲的聚集而产生向上偏差。空间矩阵有助于捕捉经典动物模型可能遗漏或错误分配的环境的精细尺度变化。在这个物种中，结果还表明，小而重要的遗传遗传力估计值不会因空间近亲的聚集而产生向上偏差。空间矩阵有助于捕捉经典动物模型可能遗漏或错误分配的环境的精细尺度变化。在这个物种中，结果还表明，小而重要的遗传遗传力估计值不会因空间近亲的聚集而产生向上偏差。