Breckheimer, I. | bioRxiv Science

bioRxiv2025

Warming winter disrupts mycorrhizal phenology and plant-fungal nutrient cycling.

Climate change is reshaping the timing of ecological processes in montane ecosystems, where short, snowpack-dependent growing seasons tightly constrain plant-microbe interactions. Arbuscular mycorrhizal (AM) fungi regulate plant acquisition of nitrogen (N) and phosphorus (P) during nutrient pulses triggered by melting snowpack. Yet the extent and consequences of warming-induced phenological asynchrony between AM fungi and host plants remains unknown. We experimentally advanced snowmelt in a subalpine meadow and monitored plant and AM fungal growth, soil nutrients, and AM fungal community composition throughout the growing season. Early snowmelt advanced plant greenness and root standing stock but suppressed AM fungal hyphal production, reducing available NH{square}{square} and PO43-. Hyphal allocation strategies shaped AM fungal temporal niches and species-specific responses to warming. Nutrient-foraging, edaphophilic AM fungi dominated early in the season, while rhizophilic AM fungi dominated later, following peak root growth. Our results reveal that fungal functional traits and nutrient dynamics govern the temporal niche partitioning of mycorrhizal fungi. By decoupling AM fungal activity from plant demand and nutrient mineralization windows, early snowmelt drives plant-fungal asynchrony. This decoupling weakens mycorrhizal symbioses and threatens nutrient retention and ecosystem stability under future warming. Such belowground temporal mismatches may determine the resilience of seasonally temperature-dependent ecosystems under warming.

ecology↗

bioRxiv2020

A new method for counting reproductive structures in digitized herbarium specimens using Mask R-CNN

Phenology-the timing of life-history events-is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current practice of manually harvesting data from individual specimens, however, greatly restricts our ability to scale-up data collection. Recent investigations have demonstrated that machine-learning approaches can facilitate this effort. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., presence/absence of flowers). Here, we use crowd-sourced phenological data of buds, flowers, and fruits from > 3000 specimens of six common wildflower species of the eastern United States (Anemone canadensis L., A. hepatica L., A. quinquefolia L., Trillium erectum L., T. grandiflorum (Michx.) Salisb., and T. undulatum Wild.) to train models using Mask R-CNN to segment and count phenological features. A single global model was able to automate the binary coding of each of the three reproductive stages with > 87% accuracy. We also successfully estimated the relative abundance of each reproductive structure on a specimen with [≥] 90% accuracy. Precise counting of features was also successful, but accuracy varied with phenological stage and taxon. Specifically, counting flowers was significantly less accurate than buds or fruits likely due to their morphological variability on pressed specimens. Moreover, our Mask R-CNN model provided more reliable data than non-expert crowd-sourcers but not botanical experts, highlighting the importance of high-quality human training data. Finally, we also demonstrated the transferability of our model to automated phenophase detection and counting of the three Trillium species, which have large and conspicuously-shaped reproductive organs. These results highlight the promise of our two-phase crowd-sourcing and machine-learning pipeline to segment and count reproductive features of herbarium specimens, thus providing high-quality data with which to investigate plant response to ongoing climatic change.

ecology↗