Temperature controls phenology in continuously flowering Protea species of subtropical Africa

PREMISE OF THE STUDY: Herbarium specimens are increasingly used as records of plant flowering phenology. However, most herbarium-based studies on plant phenology focus on taxa from temperate regions. Here, we explore flowering phenologic responses to climate in the subtropical plant genus Protea (Proteaceae), an iconic group of plants that flower year-round and are endemic to subtropical Africa. METHODS: We present a novel, circular sliding window approach to investigate phenological patterns developed for species with year-round flowering. We employ our method to evaluate the extent to which site-to- site and year-to- year variation in temperature and precipitation affect flowering dates using a database of 1727 herbarium records of 25 Protea species. We also explore phylogenetic conservatism in flowering phenology. RESULTS: We show that herbarium data combined with our sliding window approach successfully captured independently reported flowering phenology patterns (r = 0.93). Both warmer sites and warmer years were associated with earlier flowering of 3–5 days/°C, whereas precipitation variation had no significant effect on flowering phenology. Although species vary widely in phenological responsiveness, responses are phylogenetically conserved, with closely related species tending to shift flowering similarly with increasing temperature. DISCUSSION: Our results point to climate-responsive phenology for this important plant genus and indicate that the subtropical, aseasonally flowering genus Protea has temperature-driven flowering responses that are remarkably similar to those of better-studied northern temperate plant species, suggesting a generality across biomes that has not been described elsewhere.APPENDIX S1. Specimen collection frequency across day of flowering year (DOFY), a normalized version of the Julian day of year. Red vertical dashed lines correspond to January 1.APPENDIX S2. Comparison of species peak flowering season (in Julian days) recorded from herbarium specimen records versus the literature (Rebelo, 2001). Rebelo (2001) reports both a “long” season of increased flowering activity and a narrower “short” season of maximal flowering activity for each species, the centers of which are shown here relative to the peak flowering date we calculated from herbarium data as described in the text. Although the y-axis ranges from 0–365, the x-axis has a slightly broader range—given the circular nature of the calendar year, a given Julian date can take multiple values (e.g., 10 = 375), and the value that best communicates alignment with the field guide data set is shown.APPENDIX S3. Parameters used to characterize phenologic responsiveness to climate in Protea species, estimated from the mixed effects model.APPENDIX S4. Changes in flowering times of Protea species across South Africa in relation to anomalies in temperature. Statistical analysis based on mixed effects model using both spatial temperature variation (A) and temporal climate (year-to- year temperature variation) (B) as predictors, with species as random effect. Negative slopes indicate advancement of flowering with warming. Lines indicate fitted slopes for individual Protea species. Points indicate input specimen data, and have been truncated for visualization at the extremes of the y-axis range.APPENDIX S5. Species-specific statistics generated by the sliding window phenology analysis and the mixed effects model (MEM) climate analysis for each of the 25 Protea species.APPENDIX S6. Relationship between the aseasonality of species’ annual flowering phenology cycles (aseasonality index) and their estimated phenological responses to temperature variation across space and time (coefficients from the linear mixed effects model). Dashed lines show linear regressions with 95% confidence intervals shaded.APPENDIX S7. Tests of phylogenetic signal in different dimensions of Protea flowering.Texas A&M University–Corpus Christi, a National Science Foundation Graduate Research Fellowship and the National Science Foundation Postdoctoral Research Fellowship in Biology.http://www.wileyonlinelibrary.com/journal/AppsPlantSciam2020Plant Production and Soil Scienc

Machine learning using digitized herbarium specimens to advance phenological research

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

Global urban environmental change drives adaptation in white clover

Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale

Bias assessments to expand research harnessing biological collections.

Biological collections are arguably the most important resources for investigations into the impacts of human activities on biodiversity. However, the apparent opportunities presented by museum-derived datasets have not resulted in consistent or widespread use of specimens in ecology outside phenological research and species distribution modeling. We attribute this gap between opportunity and application to biases introduced by collectors, curators, and preservation practices and an imperfect understanding of these biases and how to mitigate them. To facilitate broader use of specimen-based data, we characterize collection biases across key axes and explore interactions among them. We then present a framework for determining the bias assessments needed when extracting data from biological collections. We show that bias assessments required by particular ecological studies will depend on the response variables being measured and the predictor axes of interest. We argue that quantification of biases in specimen-derived datasets is needed to facilitate the widespread application of these data

Data from: Museum specimens provide novel insights into changing plant-herbivore interactions

Mounting evidence shows that species interactions may mediate how individual species respond to climate change. However, long-term anthropogenic effects on species interactions are poorly characterized due to a lack of data. Insect herbivory is a major ecological process that represents the interaction between insect herbivores and their host plants, but historical data on insect damage to plants is particularly sparse. Here, we suggest that museum collections of insects and plants can fill key gaps in our knowledge on changing trophic interactions, including proximate mechanisms and the net outcomes of multiple global change drivers across diverse insect herbivore-plant associations. We outline theory on how global change may affect herbivores and their host plants and highlight the unique data that could be extracted from museum specimens to explore their shifting interactions. We aim to provide a framework for using museum specimens to explore how some of the most diverse co-evolved relationships are responding to climate and land use change