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Automated data-intensive forecasting of plant phenology throughout the United States.

ABSTRACT: Phenology, the timing of cyclical and seasonal natural phenomena such as flowering and leaf out, is an integral part of ecological systems with impacts on human activities like environmental management, tourism, and agriculture. As a result, there are numerous potential applications for actionable predictions of when phenological events will occur. However, despite the availability of phenological data with large spatial, temporal, and taxonomic extents, and numerous phenology models, there have been no automated species-level forecasts of plant phenology. This is due in part to the challenges of building a system that integrates large volumes of climate observations and forecasts, uses that data to fit models and make predictions for large numbers of species, and consistently disseminates the results of these forecasts in interpretable ways. Here, we describe a new near-term phenology-forecasting system that makes predictions for the timing of budburst, flowers, ripe fruit, and fall colors for 78 species across the United States up to 6 months in advance and is updated every four days. We use the lessons learned in developing this system to provide guidance developing large-scale near-term ecological forecast systems more generally, to help advance the use of automated forecasting in ecology.

SUBMITTER: Taylor SD

PROVIDER: S-EPMC9285964 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Climate change has been shown to advance spring phenology, increase the number of insect generations per year (multivoltinism) and increase pathogen infection levels. However, we lack insights into the effects of plant spring phenology and the biotic environment on the preference and performance of multivoltine herbivores and whether such effects extend into the later part of the growing season. To this aim, we used a multifactorial growth chamber experiment to examine the influence of spring phenology on plant pathogen infection, and how the independent and interactive effects of spring phenology and plant pathogen infection affect the preference and performance of multigenerational attackers (the leaf miner Tischeria ekebladella and the aphid Tuberculatus annulatus) on the pedunculate oak in the early, mid and late parts of the plant growing season. Pathogen infection was highest on late phenology plants, irrespective of whether inoculations were conducted in the early, mid or late season. The leaf miner consistently preferred to oviposit on middle and late phenology plants, as well as healthy plants, during all parts of the growing season, whereas we detected an interactive effect between spring phenology and pathogen infection on the performance of the leaf miner. Aphids preferred healthy, late phenology plants during the early season, healthy plants during the mid season, and middle phenology plants during the late season, whereas aphid performance was consistently higher on healthy plants during all parts of the growing season. Our findings highlight that the impact of spring phenology on pathogen infection and the preference and performance of insect herbivores is not restricted to the early season, but that its imprint is still present - and sometimes equally strong - during the peak and end of the growing season. Plant pathogens generally negatively affected herbivore preference and performance, and modulated the effects of spring phenology. We conclude that spring phenology and pathogen infection are two important factors shaping the preference and performance of multigenerational plant attackers, which is particularly relevant given the current advance in spring phenology, pathogen outbreaks and increase in voltinism with climate change.

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Automated data-intensive forecasting of plant phenology throughout the United States.

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