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A primary goal of ecological restoration is to increase biodiversity in degraded ecosystems. However, the success of restoration ecology is often assessed by measuring the response of a single functional group or trophic level to restoration, without considering how restoration affects multitrophic interactions that shape biodiversity. An ecosystem-wide approach to restoration is therefore necessary to understand whether animal responses to restoration, such as changes in biodiversity, are facilitated by changes in plant communities (plant-driven effects) or disturbance and succession resulting from restoration activities (management-driven effects). Furthermore, most restoration ecology studies focus on how restoration alters taxonomic diversity, while less attention is paid to the response of functional and phylogenetic diversity in restored ecosystems. Here, we compared the strength of plant-driven and management-driven effects of restoration on four animal communities (ground beetles, dung beetles, snakes, and small mammals) in a chronosequence of restored tallgrass prairie, where sites varied in management history (prescribed fire and bison reintroduction). Our analyses indicate that management-driven effects on animal communities were six-times stronger than effects mediated through changes in plant biodiversity. Additionally, we demonstrate that restoration can simultaneously have positive and negative effects on biodiversity through different pathways, which may help reconcile variation in restoration outcomes. Furthermore, animal taxonomic and phylogenetic diversity responded differently to restoration, suggesting that restoration plans might benefit from considering multiple dimensions of animal biodiversity. We conclude that metrics of plant diversity alone may not be adequate to assess the success of restoration in reassembling functional ecosystems. 

An intricate look is taken at the methods used to account for variance in minority-carrier lifetime in the silicon bottom cell of II-VI/Si tandem solar cells. A discussion on the modeling is provided. Lateral wafer variance is determined to be much less than wafer-to-wafer variance. Size testing indicates a minimum size of 4 × 4 cm is necessary for accurate results. The cleaning procedure and photoluminescence testing is described. Despite a small sample size, Si samples with CdTe deposition and CdCl2 treatment maintain over 1 ms lifetimes, enabling the Si bottom cell in II-VI/Si tandem cells to reach state-of-the-art performance. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.