1. Macrophyte species and trait assemblages from 104 minimally disturbed boreal forest lakes, covering broad environmental and geographic gradients were analysed to identify associations with environmental variables at different spatial scales: geographic context (GEO) and catchment (CATCH) and lake (LOCAL) characteristics.
2. Constrained ordination and variation partitioning were used to quantify variation in species (canonical correspondence analysis [CCA] and pCCA) and trait (redundancy analysis [RDA] and pRDA) compositions that could be explained by environmental variables, and to rank the main environmental factors associated with spatial and temporal patterns.
3. Diversity and assemblage composition correlated with spatial context and variables related to the length of the growing season, catchment forest type and with lake characteristics such as ecosystem size, lake productivity and alkalinity.
4. Variation partitioning showed that lake characteristics alone explained 53% (species) and 73.5% (traits) of the variability in macrophyte assemblages. Contrary to predictions, the shared variance component between latitude and catchment forest type (GEO&CATCH < 0.1% for both species and traits) and between latitude and lake characteristics (GEO&LOCAL = 6.7% for species and 3.9% for traits) was low.
5. Temporal variability, measured as changes in species richness, diversity and a pollution-specific index (the Trophic Macrophyte Index), using a subset of the lakes sampled on two occasions (19 lakes sampled in 2012 and 2018 and five lakes sampled in 2013 and 2019) did not differ (p > 0.05, paired t-test). Ordination showed that among-year variability in macrophyte assemblage composition was also negligible (0.3%) compared to the variability explained by GEO, CATCH and LOCAL variables. Combined, these findings indicate low species turnover in the boreal lakes of our study.
6. Responses of macrophyte species and trait assemblages and the TMI index were predictable and significantly correlated with lake characteristics associated with nutrient enrichment (Chl a, nutrients) and alkalinity supporting their use in monitoring eutrophication of boreal lakes.

中文翻译：

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当地栖息地是北方湖泊大型植物多样性和组成的时空格局的重要决定因素

1. 分析了来自 104 个受干扰最小的北方森林湖泊的大型植物物种和特征组合，涵盖了广泛的环境和地理梯度，以确定与不同空间尺度的环境变量的关联：地理背景 (GEO) 和集水区 (CATCH) 和湖泊 (LOCAL) 特征。
2. 约束排序和变异分区用于量化物种的变异（典型对应分析 [CCA] 和 pCCA）和性状（冗余分析 [RDA] 和 pRDA）组成，可以由环境变量解释，并对相关的主要环境因素进行排序具有空间和时间模式。
3. 多样性和组合构成与空间环境和与生长季节长度、集水森林类型和湖泊特征（如生态系统大小、湖泊生产力和碱度）相关的变量相关。
4. 变异划分表明，仅湖泊特征就解释了大型植物组合变异的 53%（物种）和 73.5%（特征）。与预测相反，纬度和集水森林类型（物种和性状的 GEO&CATCH < 0.1%）以及纬度和湖泊特征（物种的 GEO&LOCAL = 6.7% 和性状的 3.9%）之间的共享方差分量很低。
5. 时间变异性，测量为物种丰富度、多样性和特定污染指数（营养性大型植物指数）的变化，使用两次采样的湖泊子集（2012 年和 2018 年采样的 19 个湖泊以及 2013 和 2019 年采样的五个湖泊） ) 没有差异（p  > 0.05，配对t检验）。排序表明，与 GEO、CATCH 和 LOCAL 变量解释的变异性相比，大型植物组合组成的年间变异性也可以忽略不计（0.3%）。综合起来，这些发现表明我们研究的北方湖泊中物种周转率低。
6. 大型植物物种和性状组合以及 TMI 指数的响应是可预测的，并且与与养分富集（Chl a，养分）和碱度相关的湖泊特征显着相关，支持它们用于监测北方湖泊的富营养化。