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The Eurasian boreal forest ecosystem is a strong sink in the global carbon cycle. Satellite observations show significant change in the ecosystem in recent decades, specifically an increase in vegetation productivity since 1982 and a hiatus after 1997. Previous studies attributed this enhanced vegetation growth (also known as greening) to air temperature increases and a longer growing season, and the recent greening hiatus as a result of a warmer and drier climate. However, using satellite data, we found observational evidence that increases in summer peak growth dominated the overall greening trend and that a wetting and cooling climate during the peak growing season was the primary cause of the hiatus. Plain Language Summary Increases in vegetation growth or productivity is referred to as "greening." Scientists have found that the vegetation growth or productivity in Eurasian boreal forests increased from 1982. However, in 1997 that greening stagnated. Previous studies suggested that increases in air temperature prolonged the growing season, which may have been the driving force of the greening. Additionally, previous studies pointed to enhanced drought stress by warmer and drier summers as the reason for the greening hiatus. However, in revisiting this hypothesis we found evidence of an alternative explanation. First, we found that increases in summer peak growth, rather than a longer growing season, dominated the overall greening trends. Second, we found that wetting and cooling summer climates instigated the hiatus. Our findings suggest a more complex mechanism behind the shift in climate and vegetation growth in Eurasian boreal forests around 1997. This mechanism deserves further study.

A recent review noted important differences in the results of the local Moran's Ii statistic depending on the inference method. These differences had significant practical implications. In closing, the authors speculated the differences may be due to local spatial heterogeneity. In this article, we propose that different null hypotheses, not heteroskedasticity, generate these differences. To test this, we examine the null hypotheses implicit in common statistical significance tests of local Moran’s Ii. We design an experiment to assess the impact of local heterogeneity on tests conducted under the two most common null hypotheses. In this experiment, we analyze the relationship between measures of local variance, such as the local spatial heteroskedasticity (LOSH) statistic, and components of the local Moran’s Ii statistic. We run this experiment with controlled synthetic heteroskedastic data and with uncontrolled real‐world data with varying degrees and patterns of local heteroskedasticity. We show that, in both situations, estimates that use the same null are extremely similar, regardless of estimation method. In contrast, all estimates (regardless of the null) are moderately affected by spatial heteroskedasticity. Ultimately, this article demonstrates that there are important conceptual and computational differences about null hypothesis in local testing frameworks, and these differences can have significant practical implications. Therefore, researchers must be aware as to how their choices may shape the observed spatial patterns.

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