scholarly journals Climate shapes flowering periods across plant communities

2021 ◽  
Author(s):  
Ruby E. Stephens ◽  
Hervé Sauquet ◽  
Greg R. Guerin ◽  
Mingkai Jiang ◽  
Daniel Falster ◽  
...  
Keyword(s):  
Plant Communities ◽  
Flowering Phenology ◽  
Floral Resources ◽  
Flowering Period ◽  
Climate Conditions ◽  
Continental Scale ◽  
Temperature And Precipitation ◽  
Flowering Periods ◽  
Time Of Year ◽  
And Function

AbstractAimClimate shapes the composition and function of plant communities globally, but it remains unclear how this influence extends to floral traits. Flowering phenology, or the time period in which a species flowers, has well-studied relationships with climatic signals at the species level but has rarely been explored at a cross-community and continental scale. Here, we characterise the distribution of flowering periods (months of flowering) across continental plant communities encompassing six biomes, and determine the influence of climate on community flowering period lengths.LocationAustraliaTaxonFlowering plantsMethodsWe combined plant composition and abundance data from 629 standardised floristic surveys (AusPlots) with data on flowering period from the AusTraits database and additional primary literature for 2,983 species. We assessed abundance-weighted community mean flowering periods across biomes and tested their relationship with climatic annual means and the predictability of climate conditions using regression models.ResultsCombined, temperature and precipitation (annual mean and predictability) explain 29% of variation in continental community flowering period. Plant communities with higher mean temperatures and lower mean precipitation have longer mean flowering periods. Moreover, plant communities in climates with predictable temperatures and, to a lesser extent, predictable precipitation have shorter mean flowering periods. Flowering period varies by biome, being longest in deserts and shortest in alpine and montane communities. For instance, desert communities experience low and unpredictable precipitation and high, unpredictable temperatures and have longer mean flowering periods, with desert species typically flowering at any time of year in response to rain.Main conclusionsOur findings demonstrate the role of current climate conditions in shaping flowering periods across biomes, with implications under climate change. Shifts in flowering periods across climatic gradients reflect changes in plant strategies, affecting patterns of plant growth and reproduction as well as the availability of floral resources across the landscape.

scholarly journals Variation between some apricot varieties in regard to flowering phenology in Boldogkôváralja, Hungary

2012 ◽  
Vol 18 (1) ◽  
Author(s):  
A. Ezzat ◽  
L. Amriskó ◽  
G. G. ◽  
T. Mikita ◽  
J. Nyéki ◽  
...  
Keyword(s):  
High Temperature ◽  
Weather Conditions ◽  
Flowering Phenology ◽  
Flowering Period ◽  
Start Date ◽  
Northern Border ◽  
Flowering Periods ◽  
In The Beginning ◽  
Different Origin

The aim of this study was the estimation of blossoming of 14 apricot cultivars in Boldogkôváralja in 2009, 2010 and 2011 seasons. And this will help growers to select appropriate varieties to their weather conditions. For this target the blooming period of 19 apricot varieties of different origin was observed in three subsequent years. There was no large difference in the beginning of blooming in the different years, and the greatest variation between the start date of flowering was about 1 to 3 days as the place of experiment site near to northern border and also, length of flowering period of apricot trees is also inversely related to date when blooming started. The little differences in flowering dates and flowering periods due to the high temperature through the three seasons of study.

scholarly journals Configuration and Intercomparison of Deep Learning Neural Models for Statistical Downscaling

2019 ◽  
Author(s):  
Jorge Baño-Medina ◽  
Rodrigo Manzanas ◽  
José Manuel Gutiérrez
Keyword(s):  
Climate Change ◽  
Deep Learning ◽  
Statistical Downscaling ◽  
Linear Models ◽  
Added Value ◽  
Climate Conditions ◽  
Continental Scale ◽  
Temperature And Precipitation ◽  
Spatial Features ◽  
Learning Techniques

Abstract. Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability to learn spatial features from huge spatio-temporal datasets. However, existing studies are based on complex models, applied to particular case studies and using simple validation frameworks, which makes difficult a proper assessment of the (possible) added value offered by these techniques. As a result, these models are usually seen as black-boxes generating distrust among the climate community, particularly in climate change problems. In this paper we undertake a comprehensive assessment of deep learning techniques for continental-scale statistical downscaling, building on the VALUE validation framework. In particular, different CNN models of increasing complexity are applied for downscaling temperature and precipitation over Europe, comparing them with a few standard benchmark methods from VALUE (linear and generalized linear models) which have been traditionally used for this purpose. Besides analyzing the adequacy of different components and topologies, we also focus on their extrapolation capability, a critical point for their possible application in climate change studies. To do this, we use a warm test period as surrogate of possible future climate conditions. Our results show that, whilst the added value of CNNs is mostly limited to the reproduction of extremes for temperature, these techniques do outperform the classic ones for the case of precipitation for most aspects considered. This overall good performance, together with the fact that they can be suitably applied to large regions (e.g. continents) without worrying about the spatial features being considered as predictors, can foster the use of statistical approaches in international initiatives such as CORDEX.

scholarly journals Projections for Mexico’s Tropical Rainforests Considering Ecological Niche and Climate Change

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 119
Author(s):  
Antonio Fidel Santos-Hernández ◽  
Alejandro Ismael Monterroso-Rivas ◽  
Diódoro Granados-Sánchez ◽  
Antonio Villanueva-Morales ◽  
Malinali Santacruz-Carrillo
Keyword(s):  
Climate Change ◽  
Ecological Niche ◽  
Potential Distribution ◽  
Tropical Rainforest ◽  
Statistical Significance ◽  
Climate Change Scenarios ◽  
Climate Conditions ◽  
Tropical Regions ◽  
Temperature And Precipitation ◽  
Ecological Importance

The tropical rainforest is one of the lushest and most important plant communities in Mexico’s tropical regions, yet its potential distribution has not been studied in current and future climate conditions. The aim of this paper was to propose priority areas for conservation based on ecological niche and species distribution modeling of 22 species with the greatest ecological importance at the climax stage. Geographic records were correlated with bioclimatic temperature and precipitation variables using Maxent and Kuenm software for each species. The best Maxent models were chosen based on statistical significance, complexity and predictive power, and current potential distributions were obtained from these models. Future potential distributions were projected with two climate change scenarios: HADGEM2_ES and GFDL_CM3 models and RCP 8.5 W/m2 by 2075–2099. All potential distributions for each scenario were then assembled for further analysis. We found that 14 tropical rainforest species have the potential for distribution in 97.4% of the landscape currently occupied by climax vegetation (0.6% of the country). Both climate change scenarios showed a 3.5% reduction in their potential distribution and possible displacement to higher elevation regions. Areas are proposed for tropical rainforest conservation where suitable bioclimatic conditions are expected to prevail.

scholarly journals Configuration and intercomparison of deep learning neural models for statistical downscaling

2020 ◽  
Vol 13 (4) ◽  
pp. 2109-2124 ◽  
Author(s):  
Jorge Baño-Medina ◽  
Rodrigo Manzanas ◽  
José Manuel Gutiérrez
Keyword(s):  
Climate Change ◽  
Deep Learning ◽  
Statistical Downscaling ◽  
Linear Models ◽  
Regional Climate ◽  
Added Value ◽  
Climate Conditions ◽  
Continental Scale ◽  
Spatial Features ◽  
Learning Techniques

Abstract. Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability to learn spatial features from huge spatiotemporal datasets. However, existing studies are based on complex models, applied to particular case studies and using simple validation frameworks, which makes a proper assessment of the (possible) added value offered by these techniques difficult. As a result, these models are usually seen as black boxes, generating distrust among the climate community, particularly in climate change applications. In this paper we undertake a comprehensive assessment of deep learning techniques for continental-scale statistical downscaling, building on the VALUE validation framework. In particular, different CNN models of increasing complexity are applied to downscale temperature and precipitation over Europe, comparing them with a few standard benchmark methods from VALUE (linear and generalized linear models) which have been traditionally used for this purpose. Besides analyzing the adequacy of different components and topologies, we also focus on their extrapolation capability, a critical point for their potential application in climate change studies. To do this, we use a warm test period as a surrogate for possible future climate conditions. Our results show that, while the added value of CNNs is mostly limited to the reproduction of extremes for temperature, these techniques do outperform the classic ones in the case of precipitation for most aspects considered. This overall good performance, together with the fact that they can be suitably applied to large regions (e.g., continents) without worrying about the spatial features being considered as predictors, can foster the use of statistical approaches in international initiatives such as Coordinated Regional Climate Downscaling Experiment (CORDEX).

scholarly journals Transferability Intercomparison: An Opportunity for New Insight on the Global Water Cycle and Energy Budget

2007 ◽  
Vol 88 (3) ◽  
pp. 375-384 ◽  
Author(s):  
E. S. Takle ◽  
J. Roads ◽  
B. Rockel ◽  
W. J. Gutowski ◽  
R. W. Arritt ◽  
...  
Keyword(s):  
Energy Budget ◽  
Climate Models ◽  
Climate Model ◽  
Water Cycle ◽  
Regional Climate ◽  
Climate Conditions ◽  
Continental Scale ◽  
Global Water Cycle ◽  
Wide Range ◽  
Global Water

A new approach, called transferability intercomparisons, is described for advancing both understanding and modeling of the global water cycle and energy budget. Under this approach, individual regional climate models perform simulations with all modeling parameters and parameterizations held constant over a specific period on several prescribed domains representing different climatic regions. The transferability framework goes beyond previous regional climate model intercomparisons to provide a global method for testing and improving model parameterizations by constraining the simulations within analyzed boundaries for several domains. Transferability intercomparisons expose the limits of our current regional modeling capacity by examining model accuracy on a wide range of climate conditions and realizations. Intercomparison of these individual model experiments provides a means for evaluating strengths and weaknesses of models outside their “home domains” (domain of development and testing). Reference sites that are conducting coordinated measurements under the continental-scale experiments under the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorology Panel provide data for evaluation of model abilities to simulate specific features of the water and energy cycles. A systematic intercomparison across models and domains more clearly exposes collective biases in the modeling process. By isolating particular regions and processes, regional model transferability intercomparisons can more effectively explore the spatial and temporal heterogeneity of predictability. A general improvement of model ability to simulate diverse climates will provide more confidence that models used for future climate scenarios might be able to simulate conditions on a particular domain that are beyond the range of previously observed climates.

scholarly journals Floral Resources Sustaining African Meliponine Bee Species (Hymenoptera: Meliponini) in a Fragile Habitat of Kenya

2016 ◽  
Vol 8 (1) ◽  
pp. 42 ◽  
Author(s):  
Bridget O. Bobadoye ◽  
Paul N. Ndegwa ◽  
Lucy Irungu ◽  
Fombong Ayuka ◽  
Robert Kajobe
Keyword(s):  
Flowering Phenology ◽  
Abundant Species ◽  
Flowering Plants ◽  
Flowering Plant ◽  
Floral Resources ◽  
Habitat Gradients ◽  
Naturally Occurring ◽  
Taita Hills ◽  
Insect Pollinators ◽  
Diverse Habitat

A vast majority of insects visit flowers for food, generally termed as floral rewards. Detailed insights on flowering phenology of plants could give a hint of habitat status and the extent to which such landscapes could support insect pollinators to render both direct and indirect ecosystem services. This study monitored flowering plants which could potentially provide both pollen and nectar sources to four African meliponine bee species (Apidae: Meliponini) naturally occurring in six diverse habitat gradients of the eastern arc mountains (Taita hills) of Kenya. Blooming sequences of identified flowering plants overlapped across seasons with approximately 80 different plant species belonging to 34 families recorded, with the highest proportions from Fabaceae and Asteraceae families dominating flowering plants that were visited (67% of the visits).  A flowering calendar is presented to indicate the phenological pattern of all identified floral resources.  Hypotrigona gribodoi being the most abundant species had the highest visitation rates on plants belonging to Fabaceae and Asteraceae families, followed by Meliponula ferruginea (black), Plebeina hildebrandti and Hypotrigona ruspolii. This indicates that such fragile habitat could invariably sustain nutritional requirements essential for the survival of insect pollinators such as native meliponine bee species, though bee abundance at flowers did not significantly correlate to food availability (expressed by flowering plant richness).

scholarly journals Present temperature, precipitation and rain-on-snow climate in Svalbard

2020 ◽  
Author(s):  
Siiri Wickström ◽  
Marius O. Jonassen ◽  
John Cassano ◽  
Timo Vihma ◽  
Jørn Kristiansen
Keyword(s):  
Weather Prediction ◽  
Open Water ◽  
Low Pressure ◽  
Numerical Weather Prediction Model ◽  
The Arctic ◽  
Pressure Center ◽  
Climate Conditions ◽  
Temperature And Precipitation ◽  
Pressure Centre ◽  
Local Variability

<p>Potentially high-impact warm and wet winter conditions have become increasingly common in recent decades in the arctic archipelago of Svalbard. In this study, we document present 2m temperature, precipitation and rain-on-snow (ROS) climate conditions in Svalbard and relate them to different atmospheric circulation (AC) types. For this purpose, we utilise a set of observations together with output from the high resolution numerical weather prediction model AROME-Arctic. We find that 2m median temperatures vary the most across AC types in winter and spring, and the least in summer. Southerly and southwesterly flow is associated with 10th percentile 2m temperatures above freezing in all seasons. In terms of precipitation, we find the highest amounts and intensities with onshore flow over open water. Sea ice appears to play a strong role in the local variability in both 2m temperature and precipitation. ROS is a frequent phenomenon in the study period, in particular below 250 m ASL. In winter, ROS only occurs with AC types from the southerly sector or during the passage of a low pressure centre or trough. Most of these events occur during southwesterly flow, with a low pressure center west of Svalbard.</p><p> </p>

The impact of temperature and precipitation changes on honey bees (Apis mellifera) in the Aegean region under future climate scenarios

2021 ◽  
Author(s):  
Tim van der Schriek ◽  
Gianna Kitsara ◽  
Konstantinos V. Varotsos ◽  
Christos Giannakopoulos
Keyword(s):  
Honey Bees ◽  
Mortality Rates ◽  
Flowering Period ◽  
Extreme Temperatures ◽  
Foraging Activity ◽  
Colony Losses ◽  
Aegean Region ◽  
Ambient Temperatures ◽  
Temperature And Precipitation ◽  
Heat Extremes

<p>The Aegean region (Greece) preserves a wide genetic diversity amongst the honey bees (Apis mellifera L.) of its many islands and supports an important bee keeping industry. However, sector-specific regional impact studies, based on the latest high-resolution regional climate models (RCMs), are urgently required for developing successful local adaptation strategies for beekeeping and to preserve biodiversity under future climate change scenarios.</p><p>We evaluated direct climate change impacts on honey bees in the Aegean region through novel threshold temperature and precipitation indices, linked to critical bee behavior and colony mortality. There are strong relationships between ambient temperature and key bee colony behavior such as, for example, nest thermo-humidity regulation, annual population variability and foraging. Additionally, dry conditions and heatwaves have been empirically linked to declines in colony food stores and increased colony mortality rates. Impact projections used simulated temperature and precipitation data from an ensemble of seven RCMs under the medium (RCP4.5) and high (RCP8.5) emission scenarios for the control- (1971-2000), near future- (2031-2060) and distant future (2071-2100) periods. Simulated data were bias-adjusted using the long-term meteorological record of Naxos Island (central Aegean).</p><p>Overheating in summer constitutes a major challenge to nest temperature regulation. Thermal and humidity conditions are well-regulated in bee nests given their importance for colony health. Brood must remain at 33-36 <sup>o</sup>C and experience high relative humidity for proper development. Bees tend to start cooling nests when ambient temperatures are >25 <sup>o</sup>C. Evaporative cooling using water is of critical importance with temperatures above 35 <sup>o</sup>C and is remarkably effective in stabilising nest temperature at 36 <sup>o</sup>C, even as ambient temperatures are >60 <sup>o</sup>C. Thermoregulation is highly demanding, and brood is mainly reared during optimum periods with no/low need of regulation. Sustained high temperatures >40-45 <sup>o</sup>C cause significant colony losses. The highest foraging activity takes place in the temperature range from 12-25<sup> o</sup>C, whereas there is no activity <7<sup> o</sup>C and >43<sup> o</sup>C. Winter colony mortality rates increase when the spring flowering period experiences very low rainfall and extreme temperatures.</p><p>Future climatic change projections show significant increases in seasonal temperatures and days without precipitation, which will negatively affect the region’s bees. More frequent and severe heat-extremes will characterize seasons from spring to autumn, forcing bee colonies to cool their nests more intensively. Meanwhile, the availability of water and nectar (used for evaporative cooling) will decrease during extreme warm-dry events. The increase in heat extremes will likely lead to increased colony losses. Temperatures within the range for optimal foraging activity are less likely to occur during the flowering period. Finally, years with spring seasons characterized by very low rainfall and extreme temperatures will become more frequent in the future which may result in increased winter mortality rates.</p>

scholarly journals Species-specific flowering phenology responses to experimental warming and drought alter herbaceous plant species overlap in a temperate–boreal forest community

2020 ◽  
Author(s):  
Karen E Rice ◽  
Rebecca A Montgomery ◽  
Artur Stefanski ◽  
Roy L Rich ◽  
Peter B Reich
Keyword(s):  
Species Interactions ◽  
Summer Rainfall ◽  
Flowering Phenology ◽  
Forest Community ◽  
Herbaceous Plant ◽  
Experimental Warming ◽  
Ambient Conditions ◽  
Climate Conditions ◽  
Herbaceous Perennials ◽  
Species Specific

Abstract Background and Aims Warmer temperatures and altered precipitation patterns are expected to continue to occur as the climate changes. How these changes will impact the flowering phenology of herbaceous perennials in northern forests is poorly understood but could have consequences for forest functioning and species interactions. Here, we examine the flowering phenology responses of five herbaceous perennials to experimental warming and reduced summer rainfall over 3 years. Methods This study is part of the B4WarmED experiment located at two sites in northern Minnesota, USA. Three levels of warming (ambient, +1.6 °C and +3.1 °C) were crossed with two rainfall manipulations (ambient and 27 % reduced growing season rainfall). Key Results We observed species-specific responses to the experimental treatments. Warming alone advanced flowering for four species. Most notably, the two autumn blooming species showed the strongest advance of flowering to warming. Reduced rainfall alone advanced flowering for one autumn blooming species and delayed flowering for the other, with no significant impact on the three early blooming species. Only one species, Solidago spp., showed an interactive response to warming and rainfall manipulation by advancing in +1.6 °C warming (regardless of rainfall manipulation) but not advancing in the warmest, driest treatment. Species-specific responses led to changes in temporal overlap between species. Most notably, the two autumn blooming species diverged significantly in their flowering timing. In ambient conditions, these two species flowered within the same week. In the warmest, driest treatment, flowering occurred over a month apart. Conclusions Herbaceous species may differ in how they respond to future climate conditions. Changes to phenology may lead to fewer resources for insects or a mismatch between plants and pollinators.

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