A decade of flowering phenology of the keystone saguaro cactus (Carnegiea gigantea)

Reconstructing 120 years of climate change impacts on Joshua tree flowering

Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (Yucca brevifolia and Y. jaegeriana), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo-referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records and reveals a slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years-but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity.

Analyzing a phenological anomaly in Yucca of the southwestern United States

Yucca in the American desert Southwest typically flowers in early spring, but a well-documented anomalous bloom event occurred during an unusually cold and wet late fall and early winter 2018–2019. We used community science photographs to generate flowering presence and absence data. We fit phenoclimatic models to determine which climate variables are explanatory for normal flowering, and then we tested if the same conditions that drive normal blooming also drove the anomalous blooming event. Flowering for Yucca brevifolia (Joshua tree) and Yucca schidigera (Mojave yucca) is driven by complex, nonlinear interactions between daylength, temperature, and precipitation. To our surprise, early-season flowering odds are highest in colder and drier conditions, especially for Joshua trees, but increase with precipitation late-season. However, the models used to fit normal blooming overpredicted the number of anomalous blooms compared to what was actually observed. Thus, predicting anomalous flowering events remains a challenge for quantitative phenological models. Because our model overpredicted the number of anomalous blooms, there are likely other factors, such as biotic interactions or other seasonal factors, which may be especially important in controlling what is presumed to be rare, out-of-season flowering in desert-adapted Yucca.

Estimating flowering transition dates from status-based phenological observations: a test of methods

The scale of phenological research has expanded due to the digitization of herbarium specimens and volunteer based contributions. These data are status-based, representing the presence or absence of a specific phenophase. Modelling the progress of plant dormancy to growth and reproduction and back to dormancy requires estimating the transition dates from these status-based observations. There are several methods available for this ranging from statistical moments using the day of year to newly introduced methods using concepts from other fields. Comparing the proficiency of different estimators is difficult since true transition dates are rarely known. Here I use a recently released dataset of in-situ flowering observations of the perennial forb Echinacea angustifolia. In this dataset, due to high sampling frequency and unique physiology, the transition dates of onset, peak, and end of flowering are known to within 3 days. I used a Monte Carlo analysis to test eight different estimators across two scales using a range of sample sizes and proportion of flowering presence observations. I evaluated the estimators accuracy in predicting the onset, peak, and end of flowering at the population level, and predicting onset and end of flowering for individual plants. Overall, a method using a Weibull distribution performed the best for population level onset and end estimates, but other estimators may be more appropriate when there is a large amount of absence observations relative to presence observations. For individual estimates a method using the midway point between the first flower presence and most prior flower absence, within 7 days, is the best option as long as the restriction does not limit the final sample size. Otherwise, the Weibull method is adequate for individual estimates as well. These methods allow practitioners to effectively utilize the large amount of status-based phenological observations currently available.

Nonlinear modeling of saguaro growth rates reveals the importance of temperature for size-dependent growth

PREMISE

The saguaro cactus is an iconic species of the Sonoran Desert. Its individual growth rates have been investigated for over 100 years. Its growth dynamics have been studied using phenomenological models intended to estimate growth, but not to understand the underlying biological processes. Most studies have suggested summer rainfall as the sole factor determining saguaro growth, overlooking the influence of other factors related to the process of growth.

METHODS

We analyzed the annual growth rates for 13 saguaro populations in the Sonoran Desert using nonlinear models. These are better suited to analyze growth since they consider the fact that maximum growth rates diminish just before the onset of reproduction. We related model parameters to the local climate.

RESULTS

The most parsimonious model was the Ricker function that described growth considering cactus decline with age. Variance in temperature, rather than precipitation, was more closely related to growth. Higher variance in temperature at the beginning of the warm season was detrimental to saguaro growth.

CONCLUSIONS

Simple nonlinear equations modeled growth rate using biologically interpretable parameters related to climate factors. Because the temperature is projected to increase in both mean and variance by climate change, the population dynamics of this iconic cactus are likely to be affected.

A decade of flowering phenology of the keystone saguaro cactus (Carnegiea gigantea)

PREMISE OF THE STUDY

Phenology is the study of biological life cycle events, such as flowering and migration. Climate patterns can alter these life history events, having ecosystem-wide ramifications. For example, warmer springs are associated with earlier leaf-out for many species, impacting species interactions and growing-season carbon dynamics. While phenological research has been conducted extensively in temperate regions, relatively little is known about the phenological responses in arid and semi-arid regions.

METHODS

In this study we looked at the flowering phenology of a keystone species in the Sonoran Desert, the saguaro cactus (Carnegiea gigantea). The timing and abundance of flowering was observed on 151 individuals for 10 years at a site near Tucson, Arizona, USA. Using six phenological traits, we explored the relationship between saguaro size and flowering and the climatic drivers of flowering.

KEY RESULTS

Our analyses demonstrated how the calculation of phenological traits at the individual versus the population level can yield differing responses to climate variability, suggesting that not all studies examining the same trait (e.g., first day of bloom) are directly comparable. We found that larger cacti began flowering earlier, flowered for longer, and produced more flowers. Warmer temperatures were correlated with advanced onset and higher bloom yields, while increased precipitation appeared to delay onset and reduce bloom yields.

CONCLUSIONS

Given that climate models predict that the Southwestern USA will become increasingly warmer with more variable precipitation, saguaros may begin flowering earlier in the season and flower more intensely, which could impact pollen availability and population dynamics.