Temporal analysis of reproduction distributed in space illuminates the climate-change resiliency of toyon (Heteromeles arbutifolia)
Dakduk, D.; Yoder, J. B.
Show abstract
PremiseToyon, Heteromeles arbutifolia (Lindl.) M. Roem. (Rosaceae), is an iconic and ecologically important member of California chaparral and oak woodland communities. Toyons habitat faces changing wildfire regimes, widening variation in annual rainfall, and competition by introduced species. We used a new modeling method, temporal analysis of reproduction distributed in space (TARDIS) to examine how recent climate change alters habitat suitability for toyon. MethodsAs data for TARDIS, we annotated flowering and fruiting in images from 4,105 observations of toyon contributed to the iNaturalist crowdsourcing platform. From these records we trained Bayesian additive regression tree models relating weather to toyon flowering. We used a trained model to hindcast flowering each year back to 1900, and examined trends in hindcast flowering. For comparison, we also modeled changing habitat suitability using a conventional species distribution model (SDM) relating toyon presence to 30-year climate averages. Key resultsToyon flowering is associated with greater winter precipitation and warmer fall and winter temperatures. Our hindcast finds mean flowering intensity has been stable to slightly increasing since 1900, with greater increases at higher elevations, but also at lower latitudes. Variation in flowering intensity has also increased, especially at lower latitudes. Trends in flowering are positively correlated with changes in SDM-predicted suit-ability. ConclusionsTARDIS recovers biologically realistic predictors of toyon flowering, and hindcast changes in flowering intensity indicate the species range remains suitable after 125 years of changing climate. Overall our results indicate toyon populations remain healthy, but may have limited opportunity to migrate northwards as climate change continues.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Phenological displacement is uncommon among sympatric angiosperms 96%
- Shifts in vernalization and phenology at the rear edge hold insight into adaptation of temperate plants to future milder winters 96%
- Endemic and invasion dynamics of wild tomato species on the Galápagos Islands, across two centuries of collection records 95%
Similar papers in this journal
- Changes in quantity and timing of foliar and reproductive phenology of tropical dry-forest trees under a warming and drying climate 96%
- Phenological mismatch between trees and wildflowers: Reconciling divergent findings in two recent analyses 95%
- Herbarium records provide reliable phenology estimates in the understudied tropics 94%
Similar papers in this journal
- Lagged climate-driven range shifts at species' leading, but not trailing, range edges revealed by multispecies seed addition experiment 96%
- Spatial replication can best advance our understanding of population responses to climate 95%
- Projecting spatiotemporal bioclimatic niche dynamics of endemic Pyrenean plant species under climate change: how much will we lose? 94%
Similar papers in this journal
- Increasing prevalence of plant-fungal symbiosis across two centuries of environmental change 96%
- Delayed effects of climate on vital rates lead to demographic divergence in Amazonian forest fragments 96%
- Climate change, weather, and geography shape seed mass variation and decline across western North America 95%
Similar papers in this journal
- Disorder or a new order: how climate change affects phenological variability 96%
- Quantifying the demographic vulnerabilities of dry woodlands to climate and competition using range-wide monitoring data 95%
- Germination responses to changing rainfall timing reveal potential climate vulnerability in a clade of wildflowers 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.