Predictor Of Phenology Research Articles

Phenological shifts of abiotic events, producers and consumers across a continent

Ongoing climate change can shift organism phenology in ways that vary depending on species, habitats and climate factors studied. To probe for large-scale patterns in associated phenological change, we use 70,709 observations from six decades of systematic monitoring across the former Union of Soviet Socialist Republics. Among 110 phenological events related to plants, birds, insects, amphibians and fungi, we find a mosaic of change, defying simple predictions of earlier springs, later autumns and stronger changes at higher latitudes and elevations. Site mean temperature emerged as a strong predictor of local phenology, but the magnitude and direction of change varied with trophic level and the relative timing of an event. Beyond temperature-associated variation, we uncover high variation among both sites and years, with some sites being characterized by disproportionately long seasons and others by short ones. Our findings emphasize concerns regarding ecosystem integrity and highlight the difficulty of predicting climate change outcomes. The authors use systematic monitoring across the former USSR to investigate phenological changes across taxa. The long-term mean temperature of a site emerged as a strong predictor of phenological change, with further imprints of trophic level, event timing, site, year and biotic interactions.

Degree day-based model predicts pink bollworm phenology across geographical locations of subtropics and semi-arid tropics of India

There is a global concern about the effects of climate change driven shifts in species phenology on crop pests. Using geographically and temporally extensive data set of moth trap catches and temperatures across the cotton growing states of India, we predicted the phenology of cotton pink bollworm Pectinophora gossypiella (Saunders). Our approach was centered on growing degree days (GDD), a measure of thermal accumulation that provides a mechanistic link between climate change and species’ phenology. The phenology change was predicted by calculating absolute error associated with DD and ordinal date, an alternative predictor of phenology, for peak moth abundance. Our results show that GDD outperformed the ordinal dates in predicting peak moth abundance in 6 out of 10 selected locations. Using established thresholds of 13.0/34.0 °C, mean DD accumulated between the consecutive moth peaks across different years were estimated at 504.05 ± 4.84. Seven generations were determined for pink bollworm in a cropping season, the length of which varied between 35 and 73 days in response to temperature. Pink bollworm population reached its peak during third generation which can be the target for management actions. The study provides essential information for developing pink bollworm management strategies under climate change.

Spring wildflower phenology and pollinator activity respond similarly to climatic variation in an eastern hardwood forest.

Climate warming could disrupt species interactions if organisms' phenologies respond to climate change at different rates. Phenologies of plants and insects can be sensitive to temperature and timing of snowmelt; however, many important pollinators including ground-nesting bees have been little studied in this context. Without knowledge of the environmental cues affecting phenologies of co-occurring species, we have little ability to predict how species assemblages, and species interactions, will be affected by climate change. Here, we studied a hardwood forest understory over six years, to determine how spring temperatures, snowmelt timing, and photoperiod influence the phenology of two spring wildflowers (Anemone spp. and Trillium grandiflorum), activity of ground-nesting bees, and their temporal overlap. Surface degree-day accumulation was a better predictor of phenology for Anemone spp. (plant) and Nomada (bees) than were day of year (a proxy for photoperiod) or snowmelt date, whereas Trillium flowering appeared most sensitive to photoperiodic cues. Activity periods of Andrena and Lasioglossum bees were equally well described by degree-day accumulation and day of year. No taxon's phenology was best predicted by snowmelt date. Despite these differences among taxa in their phenological responses, timing of bee activity and flowering responded similarly to variation in snowmelt date and early spring temperatures. Furthermore, temporal overlap between flowering and bee activity was similar over the years of this study and was unaffected by variability in snowmelt date or temperature. Nevertheless, the differences among some taxa in their phenological responses suggests that diverging temporal shifts are a possibility for the future.

Phenological sequences: how early-season events define those that follow.

Plant phenology is a critical trait, as the timings of phenophases such as budburst, leafout, flowering, and fruiting, are important to plant fitness. Despite much study about when individual phenophases occur and how they may shift with climate change, little is known about how multiple phenophases relate to one another across an entire growing season. We test the extent to which early phenological stages constrain later ones, throughout a growing season, across 25 angiosperm tree species. We observed phenology (budburst, leafout, flowering, fruiting, and senescence) of 118 individual trees across 25 species, from April through December 2015. We found that early phenological events weakly constrain most later events, with the strongest constraints seen between consecutive stages. In contrast, interphase duration was a much stronger predictor of phenology, especially for reproductive events, suggesting that the development time of flowers and fruits may constrain the phenology of these events. Much of the variation in later phenological events can be explained by the timing of earlier events and by interphase durations. This highlights that a shift in one phenophase may often have cascading effects on later phases. Accurate forecasts of climate change impacts should therefore include multiple phenophases within and across years.

Plant phenological responses to a long-term experimental extension of growing season and soil warming in the tussock tundra of Alaska.

Climate warming is strongly altering the timing of season initiation and season length in the Arctic. Phenological activities are among the most sensitive plant responses to climate change and have important effects at all levels within the ecosystem. We tested the effects of two experimental treatments, extended growing season via snow removal and extended growing season combined with soil warming, on plant phenology in tussock tundra in Alaska from 1995 through 2003. We specifically monitored the responses of eight species, representing four growth forms: (i) graminoids (Carex bigellowii and Eriophorum vaginatum); (ii) evergreen shrubs (Ledum palustre, Cassiope tetragona, and Vaccinium vitis-idaea); (iii) deciduous shrubs (Betula nana and Salix pulchra); and (iv) forbs (Polygonum bistorta). Our study answered three questions: (i) Do experimental treatments affect the timing of leaf bud break, flowering, and leaf senescence? (ii) Are responses to treatments species-specific and growth form-specific? and (iii) Which environmental factors best predict timing of phenophases? Treatment significantly affected the timing of all three phenophases, although the two experimental treatments did not differ from each other. While phenological events began earlier in the experimental plots relative to the controls, duration of phenophases did not increase. The evergreen shrub, Cassiope tetragona, did not respond to either experimental treatment. While the other species did respond to experimental treatments, the total active period for these species did not increase relative to the control. Air temperature was consistently the best predictor of phenology. Our results imply that some evergreen shrubs (i.e., C.tetragona) will not capitalize on earlier favorable growing conditions, putting them at a competitive disadvantage relative to phenotypically plastic deciduous shrubs. Our findings also suggest that an early onset of the growing season as a result of decreased snow cover will not necessarily result in greater tundra productivity.

Do growing degree days predict phenology across butterfly species?

Global climate change is causing shifts in phenology across multiple species. We use a geographically and temporally extensive data set of butterfly abundance across the state of Ohio to ask whether phenological change can be predicted from climatological data. Our focus is on growing degree days (GDD), a commonly used measure of thermal accumulation, as the mechanistic link between climate change and species phenology. We used simple calculations of median absolute error associated with GDD and an alternative predictor of phenology, ordinal date, for both first emergence and peak abundance of 13 butterfly species. We show that GDD acts as a better predictor than date for first emergence in nearly all species, and for peak abundance in more than half of all species, especially univoltine species. Species with less ecological flexibility, in particular those with greater dietary specialization, had greater predictability with GDD. The new method we develop for predicting phenology using GDD offers a simple yet effective way to predict species' responses to climate change.

Early warning of West Nile virus mosquito vector: climate and land use models successfully explain phenology and abundance of Culex pipiens mosquitoes in north-western Italy.

BackgroundWest Nile Virus (WNV) is an emerging global health threat. Transmission risk is strongly related to the abundance of mosquito vectors, typically Culex pipiens in Europe. Early-warning predictors of mosquito population dynamics would therefore help guide entomological surveillance and thereby facilitate early warnings of transmission risk.MethodsWe analysed an 11-year time series (2001 to 2011) of Cx. pipiens mosquito captures from the Piedmont region of north-western Italy to determine the principal drivers of mosquito population dynamics. Linear mixed models were implemented to examine the relationship between Cx. pipiens population dynamics and environmental predictors including temperature, precipitation, Normalized Difference Water Index (NDWI) and the proximity of mosquito traps to urban areas and rice fields.ResultsWarm temperatures early in the year were associated with an earlier start to the mosquito season and increased season length, and later in the year, with decreased abundance. Early precipitation delayed the start and shortened the length of the mosquito season, but increased total abundance. Conversely, precipitation later in the year was associated with a longer season. Finally, higher NDWI early in the year was associated with an earlier start to the season and increased season length, but was not associated with abundance. Proximity to rice fields predicted higher total abundance when included in some models, but was not a significant predictor of phenology. Proximity to urban areas was not a significant predictor in any of our models. Predicted variations in start of the season and season length ranged from one to three weeks, across the measured range of variables. Predicted mosquito abundance was highly variable, with numbers in excess of 1000 per trap per year when late season temperatures were low (average 21°C) to only 150 when late season temperatures were high (average 30°C).ConclusionsClimate data collected early in the year, in conjunction with local land use, can be used to provide early warning of both the timing and magnitude of mosquito outbreaks. This potentially allows targeted mosquito control measures to be implemented, with implications for prevention and control of West Nile Virus and other mosquito borne diseases.

Maintenance of temporal synchrony between syrphid flies and floral resources despite differential phenological responses to climate

Variation in species' responses to abiotic phenological cues under climate change may cause changes in temporal overlap among interacting taxa, with potential demographic consequences. Here, we examine associations between the abiotic environment and plant-pollinator phenological synchrony using a long-term syrphid fly-flowering phenology dataset (1992-2011). Degree-days above freezing, precipitation, and timing of snow melt were investigated as predictors of phenology. Syrphids generally emerge after flowering onset and end their activity before the end of flowering. Neither flowering nor syrphid phenology has changed significantly over our 20-year record, consistent with a lack of directional change in climate variables over the same time frame. Instead we document interannual variability in the abiotic environment and phenology. Timing of snow melt was the best predictor of flowering onset and syrphid emergence. Snow melt and degree-days were the best predictors of the end of flowering, whereas degree-days and precipitation best predicted the end of the syrphid period. Flowering advanced at a faster rate than syrphids in response to both advancing snow melt and increasing temperature. Different rates of phenological advancements resulted in more days of temporal overlap between the flower-syrphid community in years of early snow melt because of extended activity periods. Phenological synchrony at the community level is therefore likely to be maintained for some time, even under advancing snow melt conditions that are evident over longer term records at our site. These results show that interacting taxa may respond to different phenological cues and to the same cues at different rates but still maintain phenological synchrony over a range of abiotic conditions. However, our results also indicate that some individual plant species may overlap with the syrphid community for fewer days under continued climate change. This highlights the role of interannual variation in these flower-syrphid interactions and shows that species-level responses can differ from community-level responses in nonintuitive ways.

General phenological model to characterise the timing of flowering and veraison of Vitis vinifera L.

Background and Aims: Phenological models, which are based on responses of the plant to temperature, are useful tools to predict grapevine (Vitis vinifera L.) phenology in various climate conditions. This study aimed to develop a single process-based phenological model at the species level to predict two important stages of development for V. vinifera L.: flowering and veraison. Methods and Results: Three different phenological models were tested and the model that gave the best results was optimised for its parameters. The chosen model Spring Warming was found optimal with regard to the trade-off between parsimony of input parameters and efficiency. The base temperature (Tb) of 0°C calculated from the 60th day (t0) of the year (for the Northern hemisphere) was found to be the most optimal parameter set tested. This model henceforth referred to as the Grapevine Flowering Veraison model (GFV) was successfully validated at the varietal level using an independent dataset. Conclusions: A general phenological model, GFV, has been successfully developed to characterise the timing of flowering and veraison for the grapevine. Significance of the Study: The model is simple for the user, can be successfully applied to many varieties and can be used as an easy predictor of phenology for different varieties under climate change scenarios.

Local Temperature Fine-Tunes the Timing of Spring Migration in Birds

Evidence for climate-driven phenological changes is rapidly increasing at all trophic levels. Our current poor knowledge of the detailed control of bird migration from the level of genes and hormonal control to direct physiological and behavioral responses hampers our ability to understand and predict consequences of climatic change for migratory birds. In order to better understand migration phenology and adaptation in environmental changes, we here assess the scale at which weather affects timing of spring migration in passerine birds. We use three commonly used proxies of spring-time climatic conditions: (1) vegetation "greenness" (NDVI) in Europe, (2) local spring temperatures in northern Europe, and (3) the North Atlantic Oscillation Index (NAO) as predictors of the phenology of avian migration as well as the strength of their effect on different subsets of populations and the dependence of correlations on species-specific migratory strategy. We analyze phenological patterns of the entire spring migration period in 12 Palaearctic passerine species, drawing on long-term data collected at three locations along a longitudinal gradient situated close to their northern European breeding area. Local temperature was the best single predictor of phenology with the highest explanatory power achieved in combination with NAO. Furthermore, early individuals are more affected by climatic variation compared to individuals on later passage, indicating that climatic change affects subsets of migratory populations differentially. Species wintering closer to the breeding areas were affected more than were those travelling longer distances and this pattern was strongest for the earliest subsets of the population. Overall, our results suggest that at least early subsets of the population are affected by local conditions and early birds use local conditions to fine-tune the date of their spring arrival while individuals arriving later are driven by other factors than local conditions e.g. endogenous control. Understanding what cues migratory organisms use to arrive at an optimum time is important for increasing our knowledge of fundamental issues like decision making in organisms during migration and is crucial for future protection of migratory organisms.

Increasing the robustness of phenological models for Vitis vinifera cv. Chardonnay

Phenological models are important tools for planning viticultural practices in the short term and for projecting the impact of climate change on grapevine (Vitis vinifera) in the long term. However, the difficulties in obtaining phenological models which provide accurate predictions on a regional scale prevent them from being exploited to their full potential. The aim of this work was to obtain a robust phenological model for V. vinifera cv. Chardonnay. During calibration of the sub-models for budburst, flowering and veraison we implemented a series of measures to prevent overfitting and to give greater physiological meaning to the models. Among these were the use of experimental information on the response of Chardonnay to forcing temperatures, restriction of parameter space into physiologically meaningful limits prior to calibration, and simplification of the previously selected sub-models. The resulting process-based model had good internal validity and a good level of accuracy in predicting phenological events from external datasets. Model performance was especially high for the prediction of flowering and veraison, and comparison with other models confirmed it as a better predictor of phenology, even in extremely warm years. The modelling study highlighted a different phenological behaviour at the only mountain station, Cembra. We hypothesised that phenotypical plasticity could lead to growth rates adapting to a lower mean temperature, a mechanism not usually accounted for by phenological models.

Predictors of plant phenology in a diverse high‐latitude alpine landscape: growth forms and topography

AbstractQuestion: Different plant growth forms may have distinctly different functioning in ecosystems. Association of phenological patterns with growth form will therefore help elucidate the role of phenology in an ecosystem. We ask whether growth forms of common vascular plants differ in terms of vegetative and flowering phenology, and if such phenological differences are consistent across environmental gradients caused by landscape‐scale topography.Location: A high‐latitude alpine landscape in Finnmark County, Norway (70°N).Methods: We assessed vegetative and flowering phenology repeatedly in five growth forms represented by 11 common vascular plant species across an altitudinal gradient and among differing slope aspects.Results: Species phenology clustered well according to growth form, and growth form strongly explained variation in both flowering and vegetative phenology. Altitude and aspect were poor predictors of phenological variation. Vegetative phenology of the growth forms, ranked from slowest to fastest, was in the order evergreen shrubs <sedges‐deciduous shrubs <grasses <forbs, while a reverse ranking was found for flowering phenology.Conclusion: Growth form‐specific phenological patterns are associated with fundamentally different abilities for resource acquisition and resource conservation. The weak effect of landscape‐scale topographic factors indicates that variation within growth forms is mainly influenced by local environmental factors not accounted for in this study. On the basis of these results, we argue that growth forms should be considered as predictors of phenology together with the customary use of topography and normalized difference vegetation index, especially when assessing the role of phenology in an ecosystem.

Predicting phenological development in winter wheat

Accurate prediction of phenological development is important in the winter wheat Triticum aestivum agroecosystem. From a practical perspective, applications of pesticides and fertil- izers are carried out at specific phenological stages. In crop-simulation modeling, the prediction of yield components (kernel number and kernel weight) and wheat-grain yield relies on accurate pre- diction of phenology. In this study, a nonlinear multiplicative model by Wang & Engel (WE) for pre- dicting phenological development in differing winter wheat cultivars was evaluated using data from a 3 yr field experiment. In the vegetative phase (emergence to anthesis) the daily development rate (r) was simulated based on the product of a maximum development rate (Rmax) in the vegetative phase, a temperature response function (ƒ(T)), a photoperiod response function (ƒ(P)), and a vernal- ization response function (ƒ(V)). ƒ(T) was a nonlinear function of the 3 cardinal temperatures for phe- nological development (minimum, Tmin, optimum, Topt, and maximum, Tmax). ƒ(P) was an exponential function of the actual and critical photoperiods and a sensitivity parameter unique to each cultivar. ƒ(V) was calculated using ƒ(T) based on the cardinal temperatures for vernalization (Tmin,vn, Topt,vn, and Tmax,vn). In the reproductive phase, r was simulated based on the product of Rmax for the repro- ductive phase and ƒ(T). Predictions from this nonlinear model were compared to predictions from the phenology submodel of CERES-Wheat V3.0 (CW3). The nonlinear model performed very well for predicting phenological development in the 3 winter wheat cultivars, the mean root mean square error (RMSE) ranged from 2.9 to 4.1 d from booting to maturity. For the CW3 model, the mean RMSE ranged from 4.8 to 5.9 d for the same phenological stages. The WE model predicted double ridge with a mean RMSE of 7.3 d. Both models predicted terminal spikelet with a mean RMSE ranging from 6.2 to 7.1 d. The WE model was generally a better predictor of phenology between booting and maturity than the CW3 model.