123 research outputs found

    Native bee habitat restoration: key ecological considerations from recent North American literature

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    Habitat loss is a primary driver of global biodiversity decline, negatively impacting many species, including native bees. One approach to counteract the consequences of habitat loss is through restoration, which includes the transformation of degraded or damaged habitats to increase biodiversity. In this review, we survey bee habitat restoration literature over the last 14 years to provide insights into how best to promote bee diversity and abundance through the restoration of natural landscapes in North America. We highlight relevant questions and concepts to consider throughout the various stages of habitat restoration projects, categorizing them into pre-, during-, and post-restoration stages. We emphasize the importance of planning species- and site-specific strategies to support bees, including providing floral and non-floral resources and increasing nest site availability. Lastly, we underscore the significance of conducting evaluations and long-term monitoring following restoration efforts. By identifying effective restoration methods, success indicators, and areas for future research, our review presents a comprehensive framework that can guide land managers during this urgent time for bee habitat restoration

    Flowering Date of Taxonomic Families Predicts Phenological Sensitivity to Temperature: Implications for Forecasting the Effects of Climate Change on Unstudied Taxa

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    Premise of the study: Numerous long-term studies in seasonal habitats have tracked interannual variation in fi rst fl owering date (FFD) in relation to climate, documenting the effect of warming on the FFD of many species. Despite these efforts, long-term phenological observations are still lacking for many species. If we could forecast responses based on taxonomic affi nity, however, then we could leverage existing data to predict the climate-related phenological shifts of many taxa not yet studied; Methods: We examined phenological time series of 1226 species occurrences (1031 unique species in 119 families) across seven sites in North America and England to determine whether family membership (or family mean FFD) predicts the sensitivity of FFD to standardized interannual changes in temperature and precipitation during seasonal periods before fl owering and whether families differ signifi cantly in the direction of their phenological shifts; Key results: Patterns observed among species within and across sites are mirrored among family means across sites; earlyfl owering families advance their FFD in response to warming more than late-fl owering families. By contrast, we found no consistent relationships among taxa between mean FFD and sensitivity to precipitation as measured here; Conclusions: Family membership can be used to identify taxa of high and low sensitivity to temperature within the seasonal, temperate zone plant communities analyzed here. The high sensitivity of early-fl owering families (and the absence of earlyfl owering families not sensitive to temperature) may refl ect plasticity in fl owering time, which may be adaptive in environments where early-season conditions are highly variable among years

    Old Plants, New Tricks:Phenological Research Using Herbarium Specimens

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    The timing of phenological events, such as leaf-out and flowering, strongly influence plant success and their study is vital to understanding how plants will respond to climate change. Phenological research, however, is often limited by the temporal, geographic, or phylogenetic scope of available data. Hundreds of millions of plant specimens in herbaria worldwide offer a potential solution to this problem, especially as digitization efforts drastically improve access to collections. Herbarium specimens represent snapshots of phenological events and have been reliably used to characterize phenological responses to climate. We review the current state of herbarium-based phenological research, identify potential biases and limitations in the collection, digitization, and interpretation of specimen data, and discuss future opportunities for phenological investigations using herbarium specimens

    Machine learning using digitized herbarium specimens to advance phenological research

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    Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth
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