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Delta blue intensity is a commonly used method to correct for the heartwood-sapwood color change in blue intensity (BI) measurements. It is based on the assumption that the heartwood-sapwood color change is similar in both earlywood and latewood. This assumption has not been supported physiologically. Furthermore, delta BI may confound the climate signals in earlywood and latewood BI as it is technically a linear combination of the other two. Here, instead of using delta BI, we used change point detection to identify the heartwood-sapwood transition, and corrected for the color change by rescaling the mean and variance of BI measurements after the transition to those immediately before. We tested three different change point detection methods and found that they agreed well with one another. Importantly, our approach preserves the climate signals in both earlywood and latewood BI data, while delta BI causes a total loss of climate signals in our test case. Therefore, we suggest that change point detection should be used instead of delta BI to account for the heartwood-sapwood color change. 

Abstract Growth in the green jobs sector has increased demand for college graduates who are prepared to enter the workforce with interdisciplinary sustainability skills. Simultaneously, scholarly calls for interdisciplinary collaboration in the service of addressing the societal challenges of enhancing resilience and sustainability have also increased in recent years. However, developing, executing, and assessing interdisciplinary content and skills at the post-secondary level has been challenging. The objective of this paper is to offer the Food-Energy-Water (FEW) Nexus as a powerful way to achieve sustainability competencies and matriculate graduates who will be equipped to facilitate the transformation of the global society by meeting the targets set by the United Nations Sustainable Development Goals. The paper presents 10 curricular design examples that span multiple levels, including modules, courses, and programs. These modules enable clear evaluation and assessment of key sustainability competencies, helping to prepare graduates with well-defined skillsets who are equipped to address current and future workforce needs. 

Abstract AimClimate and disturbance alter forest dynamics, from individual trees to biomes and from years to millennia, leaving legacies that vary with local, meso‐ and macroscales. Motivated by recent insights in temperate forests, we argue that temporal and spatial extents equivalent to that of the underlying drivers are necessary to characterize forest dynamics across scales. We focus specifically on characterizing mesoscale forest dynamics because they bridge fine‐scale (local) processes and the continental scale (macrosystems) in ways that are highly relevant for climate change science and ecosystem management. We revisit ecological concepts related to spatial and temporal scales and discuss approaches to gain a better understanding of climate–forest dynamics across scales. LocationEastern USA. Time periodLast century to present. Major taxa studiedTemperate broadleaf forests. MethodsWe review regional literature of past tree mortality studies associated with climate to identify mesoscale climate‐driven disturbance events. Using a dynamic vegetation model, we then simulate how these forests respond to a typical climate‐driven disturbance. ResultsBy identifying compound disturbance events from both a literature review and simulation modelling, we find that synchronous patterns of drought‐driven mortality at mesoscales have been overlooked within these forests. Main conclusionsAs ecologists, land managers and policy‐makers consider the intertwined drivers of climate and disturbance, a focus on spatio‐temporal scales equivalent to those of the drivers will provide insight into long‐term forest change, such as drought impacts. Spatially extensive studies should also have a long temporal scale to provide insight into pathways for forest change, evaluate predictions from dynamic forest models and inform development of global vegetation models. We recommend integrating data collected from spatially well‐replicated networks (e.g., archaeological, historical or palaeoecological data), consisting of centuries‐long, high‐resolution records, with models to characterize better the mesoscale response of forests to climate change in the past and in the future.