scholarly journals Ecosystem physio-phenology revealed using circular statistics

2019 ◽  
Author(s):  
Daniel E. Pabon-Moreno ◽  
Talie Musavi ◽  
Mirco Migliavacca ◽  
Markus Reichstein ◽  
Christine Römermann ◽  
...  
Keyword(s):  
Climate Variability ◽  
Gross Primary Production ◽  
Ecosystem Dynamics ◽  
Circular Statistics ◽  
Individual Species ◽  
Vegetation Types ◽  
Linear Predictor ◽  
Growing Seasons ◽  
Circular Model ◽  
Circular Regression

Abstract. Quantifying responses of vegetation phenology to climate variability is a key prerequisite to predict shifts in how ecosystem dynamics due to climate change. So far, many studies have focused on responses of classical phenological events (e.g. budburst or flowering) to climatic variability for individual species. Comparatively little is known on physio-phenological events such as the timing of the maximum gross primary production (DOYGPPmax). However, understanding this type of physio-phenological phenomena is an essential element in predicting the response of the terrestrial carbon cycle to climate variability. In this study, we aim to understand how DOYGPPmax depends on climate drivers across 52 eddy-covariance (EC) sites in the FLUXNET network for different regions of the world. Most phenological studies rely on linear methods that cannot be generalized across both hemispheres and therefore do not allow for deriving general rules that can be applied for future predictions. Here we explore a new class of circular-linear (here called circular) regression approach that may show a path ahead. Circular regression allows relating circular variables (in our case phenological events) to linear predictor variables (e.g. climate conditions). As a proof of concept, we compare the performance of linear and circular regression to recover original coefficients of a predefined circular model on artificial and EC data. We then quantify the sensitivity of DOYGPPmax to air temperature, short-wave incoming radiation, precipitation and vapor pressure deficit using circular regressions. Finally, we evaluate the predictive power of the regression models for different vegetation types. Our results show that the DOYGPPmax of each FLUXNET site has a unique signature of climatic sensitivities. Overall radiation and temperature are the most relevant controlling factors of DOYGPPmax across sites. The circular approach gives us new insights at the site level. In a Mediterranean shrub-land, for instance, we find that the two growing seasons are controlled by different climatic factors. Although the sensitivity of the DOYGPPmax to the climate drivers is very site specific, it is possible to extrapolate the circular regression model across vegetation types. From a methodological point of view, our results reveal that circular regression is a robust alternative to conventional phenological analytic frameworks. In particular global analyses can benefit, where phase shifts play a role or double peaked growing seasons may occur.

scholarly journals Ecosystem physio-phenology revealed using circular statistics

2020 ◽  
Vol 17 (15) ◽  
pp. 3991-4006
Author(s):  
Daniel E. Pabon-Moreno ◽  
Talie Musavi ◽  
Mirco Migliavacca ◽  
Markus Reichstein ◽  
Christine Römermann ◽  
...  
Keyword(s):  
Climate Variability ◽  
Vapor Pressure ◽  
Vapor Pressure Deficit ◽  
Ecosystem Dynamics ◽  
Circular Statistics ◽  
Individual Species ◽  
Vegetation Types ◽  
Linear Predictor ◽  
Circular Model ◽  
Circular Regression

Abstract. Quantifying how vegetation phenology responds to climate variability is a key prerequisite to predicting how ecosystem dynamics will shift with climate change. So far, many studies have focused on responses of classical phenological events (e.g., budburst or flowering) to climatic variability for individual species. Comparatively little is known on the dynamics of physio-phenological events such as the timing of maximum gross primary production (DOYGPPmax), i.e., quantities that are relevant for understanding terrestrial carbon cycle responses to climate variability and change. In this study, we aim to understand how DOYGPPmax depends on climate drivers across 52 eddy covariance (EC) sites in the FLUXNET network for different regions of the world. Most phenological studies rely on linear methods that cannot be generalized across both hemispheres and therefore do not allow for deriving general rules that can be applied for future predictions. One solution could be a new class of circular–linear (here called circular) regression approaches. Circular regression allows circular variables (in our case phenological events) to be related to linear predictor variables as climate conditions. As a proof of concept, we compare the performance of linear and circular regression to recover original coefficients of a predefined circular model for artificial data. We then quantify the sensitivity of DOYGPPmax across FLUXNET sites to air temperature, shortwave incoming radiation, precipitation, and vapor pressure deficit. Finally, we evaluate the predictive power of the circular regression model for different vegetation types. Our results show that the joint effects of radiation, temperature, and vapor pressure deficit are the most relevant controlling factor of DOYGPPmax across sites. Woody savannas are an exception, where the most important factor is precipitation. Although the sensitivity of the DOYGPPmax to climate drivers is site-specific, it is possible to generalize the circular regression models across specific vegetation types. From a methodological point of view, our results reveal that circular regression is a robust alternative to conventional phenological analytic frameworks. The analysis of phenological events at the global scale can benefit from the use of circular statistics. Such an approach yields substantially more robust results for analyzing phenological dynamics in regions characterized by two growing seasons per year or when the phenological event under scrutiny occurs between 2 years (i.e., DOYGPPmax in the Southern Hemisphere).

Detecting Climate Fingerprints in Southern Ocean Carbon using a Biogeochemical Ocean Model

2021 ◽  
Author(s):  
Rebecca Wright ◽  
Corinne Le Quéré ◽  
Erik Buitenhuis ◽  
Dorothee Bakker
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Southern Ocean ◽  
Observational Data ◽  
Ecosystem Dynamics ◽  
Global Ocean ◽  
Subsurface Waters ◽  
Climatic Drivers ◽  
Ocean Carbon ◽  
And Storage

<p>The Southern Ocean plays an important role in the uptake, transport and storage of carbon by the global oceans. These properties are dominated by the response to the rise in anthropogenic CO<sub>2</sub> in the atmosphere, but they are modulated by climate variability and climate change. Here we explore the effect of climate variability and climate change on ocean carbon uptake and storage in the Southern Ocean. We assess the extent to which climate change may be distinguishable from the anthropogenic CO<sub>2</sub> signal and from the natural background variability. We use a combination of biogeochemical ocean modelling and observations from the GLODAPv2020 database to detect climate fingerprints in dissolved inorganic carbon (DIC).</p><p>We conduct an ensemble of hindcast model simulations of the period 1920-2019, using a global ocean biogeochemical model which incorporates plankton ecosystem dynamics based on twelve plankton functional types. We use the model ensemble to isolate the changes in DIC due to rising anthropogenic CO<sub>2</sub> alone and the changes due to climatic drivers (both climate variability and climate change), to determine their relative roles in the emerging total DIC trends and patterns. We analyse these DIC trends for a climate fingerprint over the past four decades, across spatial scales from the Southern Ocean, to basin level and down to regional ship transects. Highly sampled ship transects were extracted from GLODAPv2020 to obtain locations with the maximum spatiotemporal coverage, to reduce the inherent biases in patchy observational data. Model results were sampled to the ship transects to compare the climate fingerprints directly to the observational data.</p><p>Model results show a substantial change in DIC over a 35-year period, with a range of more than +/- 30 µmol/L. In the surface ocean, both anthropogenic CO<sub>2</sub> and climatic drivers act to increase DIC concentration, with the influence of anthropogenic CO<sub>2</sub> dominating at lower latitudes and the influence of climatic drivers dominating at higher latitudes. In the deep ocean, the anthropogenic CO<sub>2</sub> generally acts to increase DIC except in the subsurface waters at lower latitudes, while climatic drivers act to decrease DIC concentration. The combined fingerprint of anthropogenic CO<sub>2</sub> and climatic drivers on DIC concentration is for an increasing trend at the surface and decreasing trends in low latitude subsurface waters. Preliminary comparison of the model fingerprints to observational ship transects will also be presented.</p>

scholarly journals A Comparison of OCO-2 SIF, MODIS GPP, and GOSIF Data from Gross Primary Production (GPP) Estimation and Seasonal Cycles in North America

2020 ◽  
Vol 12 (2) ◽  
pp. 258 ◽  
Author(s):  
Ruonan Qiu ◽  
Ge Han ◽  
Xin Ma ◽  
Hao Xu ◽  
Tianqi Shi ◽  
...  
Keyword(s):  
North America ◽  
Primary Production ◽  
Vegetation Type ◽  
Gross Primary Production ◽  
Remotely Sensed ◽  
Seasonal Cycles ◽  
Vegetation Types ◽  
Growth Trend ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Highly Correlated

Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13–0.88, p < 0.001; OCO-2 SIF: R2 = 0.11–0.99, p < 0.001; MODIS GPP: R2 = 0.15–0.79, p < 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP.

scholarly journals Circular Modelling of Circumplex Measurements for Interpersonal Behavior

Assessment ◽  
2019 ◽  
pp. 107319111985840
Author(s):  
Jolien Cremers ◽  
Helena J. M. Pennings ◽  
Tim Mainhard ◽  
Irene Klugkist
Keyword(s):  
Repeated Measures ◽  
Interpersonal Behavior ◽  
Interpersonal Circumplex ◽  
Data Sets ◽  
Circular Model ◽  
Fixed And Random Effects ◽  
Different Types ◽  
Agency And Communion ◽  
Circular Regression ◽  
Analyze Data

This article describes a new way to analyze data from the interpersonal circumplex (IPC) for interpersonal behavior. Instead of analyzing Agency and Communion separately or analyzing the IPC’s octants, we propose using a circular regression model that allows us to investigate effects on a blend of Agency and Communion. The proposed circular model is called a projected normal (PN) model. We illustrate the use of a PN mixed-effects model on three repeated measures data sets with circumplex measurements from interpersonal and educational psychology. This model allows us to detect different types of patterns in the data and provides a more valid analysis of circumplex data. In addition to being able to investigate the effect on the location (mean) of scores on the IPC, we can also investigate effects on the spread (variance) of scores on the IPC. We also introduce new tools that help interpret the fixed and random effects of PN models.

scholarly journals EXPLORING STRIP INTERCROPPING POTENTIALS OF MAIZE-PULSE CROPS TO FIGHT CLIMATE VARIABILITY IMPACTS IN DRYLAND AREAS

2017 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
I Komang Damar Jaya ◽  
Sudirman Sudirman ◽  
Rosmilawati Rosmilawati
Keyword(s):  
Climate Variability ◽  
Relative Yield ◽  
Field Capacity ◽  
Cost Ratio ◽  
Maize Production ◽  
Growing Period ◽  
Pulse Crops ◽  
Growing Seasons ◽  
Recent Climate ◽  
Strip Intercropping

Recent climate variability affects maize production in dryland areas. This study aimed to explore potentials of strip intercropping of maize-pulse crops in improving productivity of dryland areas. The study was conducted in dryland area of Gumantar village, North Lombok (8.253654 S, 116.285695 E). Soil in that area was categorized as poor soil with the following properties: 0.46% organic matter, 0.05% N total (Kejdhal), available P 11.25 ppm (Olsen) and exchangeable K 0.77 me%, pH 7.0 and field capacity 29% (%/V). Rainfall data were collected during the growing seasons of 2016/2016 and 2016/2017. A field experiment of maize-pulse crops strip intercropping was conducted during a dry season of 2016. The component crops in the strip intercropping were maize NK212, maize NK7328, mungbean Vima-1 and groundnut Hypoma-1. All component crops were grown as monocropping and strip intercropping of maize-pulse crops in 8.4 x 5.0m plot size for each treatment. To measure productivity of the strip intercropping, relative yield total (RYT) and benefit to cost ratio (B/C) were calculated. They were great variations in rainfall in the last two years. From the experiment, data showed that all the strip intercropping treatments have RYT and B/C values >1 meaning that strip intercropping of maize-pulse crops is more productive than monocropping and is feasible to be practiced in dryland areas. With the short growing period and their drought tolerant nature of the pulse crops, especially mungbean, the strip intercropping can be used to fight climate variability impacts in dryland areas.

Pasture management in semi-arid tropical woodlands: dynamics of perennial grasses

2007 ◽  
Vol 29 (1) ◽  
pp. 87 ◽  
Author(s):  
John G. McIvor
Keyword(s):  
Perennial Grasses ◽  
Individual Species ◽  
Stocking Rate ◽  
Pasture Management ◽  
Management Options ◽  
Exotic Grasses ◽  
Heteropogon Contortus ◽  
Basal Cover ◽  
Growing Seasons ◽  
North East

The effects of a range of pasture management options (introduced legumes and grasses, superphosphate, timber treatment, cultivation before sowing and stocking rate) on the basal cover of perennial grasses were measured from 1982 to 1991 at two sites, ‘Hillgrove’ and ‘Cardigan’, near Charters Towers, in north-east Queensland. Colonisation and survival of eight native and exotic grasses were followed in permanent quadrats in a subset of treatments. Overall, there were significant changes in total basal cover of plots between years and with tree killing, but no significant differences in sown pastures, fertiliser or stocking rate. Basal cover increased when defoliation levels were less than 40% but increases were smaller at higher levels of defoliation and basal cover often declined when defoliation was greater than 60%. Basal cover declined when growing seasons were <10 weeks, remained static with 10–15 weeks growth, and increased when growing seasons were 16 weeks or longer. There was some colonisation in all years but large differences between years. The differences in colonisation between systems were generally small but there was a general trend for higher colonisation at higher stocking rates. Bothriochloa ewartiana (Domin) C.E.Hubb. and Chrysopogon fallax S.T.Blake had low, Heteropogon contortus (L.) P.Beauv. ex Roem.&Schult., Cenchrus ciliaris L. and Aristida spp. had intermediate, and Bothriochloa pertusa (L.) A.Camus and Urochloa mosambicensis (Hack.) Dandy had high colonising ability. Survival of individual species was generally similar at both sites except for Urochloa mosambicensis. Heteropogon contortus and U. mosambicensis at ‘Hillgrove’ were short-lived (<10% survival after 4 years), B. ewartiana, Themeda triandra Forssk. and Aristida spp. had intermediate survival (10–50%), and C. ciliaris, C. fallax, B. pertusa and U. mosambicensis at ‘Cardigan’ were long-lived (>50% survival). Annual survival rates increased with plant age, were higher in good growing seasons than in poor seasons, were higher for large plants than small plants, and were lower at high defoliation levels than where defoliation was less severe. The differences between species in ability to colonise and survive, and the small influence of management compared to seasonal effects on survival, are discussed to explain species performance in pastures.

scholarly journals Plant communities as drivers of soil respiration: pathways, mechanisms, and significance for global change

2011 ◽  
Vol 8 (8) ◽  
pp. 2047-2061 ◽  
Author(s):  
D. B. Metcalfe ◽  
R. A. Fisher ◽  
D. A. Wardle
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Plant Traits ◽  
Terrestrial Ecosystems ◽  
Climate Impacts ◽  
Individual Species ◽  
Global Changes ◽  
Individual Plant ◽  
Vegetation Types ◽  
Species Invasions

Abstract. Understanding the impacts of plant community characteristics on soil carbon dioxide efflux (R) is a key prerequisite for accurate prediction of the future carbon (C) balance of terrestrial ecosystems under climate change. However, developing a mechanistic understanding of the determinants of R is complicated by the presence of multiple different sources of respiratory C within soil – such as soil microbes, plant roots and their mycorrhizal symbionts – each with their distinct dynamics and drivers. In this review, we synthesize relevant information from a wide spectrum of sources to evaluate the current state of knowledge about plant community effects on R, examine how this information is incorporated into global climate models, and highlight priorities for future research. Despite often large variation amongst studies and methods, several general trends emerge. Mechanisms whereby plants affect R may be grouped into effects on belowground C allocation, aboveground litter properties and microclimate. Within vegetation types, the amount of C diverted belowground, and hence R, may be controlled mainly by the rate of photosynthetic C uptake, while amongst vegetation types this should be more dependent upon the specific C allocation strategies of the plant life form. We make the case that plant community composition, rather than diversity, is usually the dominant control on R in natural systems. Individual species impacts on R may be largest where the species accounts for most of the biomass in the ecosystem, has very distinct traits to the rest of the community and/or modulates the occurrence of major natural disturbances. We show that climate vegetation models incorporate a number of pathways whereby plants can affect R, but that simplifications regarding allocation schemes and drivers of litter decomposition may limit model accuracy. We also suggest that under a warmer future climate, many plant communities may shift towards dominance by fast growing plants which produce large quantities of nutrient rich litter. Where this community shift occurs, it could drive an increase in R beyond that expected from direct climate impacts on soil microbial activity alone. We identify key gaps in knowledge and recommend them as priorities for future work. These include the patterns of photosynthate partitioning amongst belowground components, ecosystem level effects of individual plant traits, and the importance of trophic interactions and species invasions or extinctions for ecosystem processes. A final, overarching challenge is how to link these observations and drivers across spatio-temporal scales to predict regional or global changes in R over long time periods. A more unified approach to understanding R, which integrates information about plant traits and community dynamics, will be essential for better understanding, simulating and predicting patterns of R across terrestrial ecosystems and its role within the earth-climate system.

Robber flies (Insecta: Diptera: Asilidae) of Wyoming, USA with keys to genera and species

Zootaxa ◽  
2019 ◽  
Vol 4662 (1) ◽  
pp. 1-126
Author(s):  
ROBERT J. LAVIGNE ◽  
D. STEVE DENNIS
Keyword(s):  
Individual Species ◽  
Vegetation Types ◽  
Grassland Vegetation ◽  
Robber Flies ◽  
Species Descriptions

There are 171 species of robber flies recorded for Wyoming, USA, including three newly described species (Cyrtopogon hollandi sp. nov., C. martini sp. nov., and Stenopogon graminis sp. nov.) in this paper, in 10 of the 14 recognized subfamilies. The largest numbers of species belong to the Asilinae (61) followed by Brachyrhopalinae (35), Laphriinae (23), Stenopogoninae (19), Dasypogoninae (9), Stichopogoninae (9), Leptogastrinae (8), Dioctriinae (3), Willistonininae (3), and Trigonomiminae (1). The most species (136) occur in one or more of the shrub/grassland vegetation types, with fewer species occurring in the grassland vegetation types (130), the forest (78 species) and in the shrub (24 species) vegetation types. Keys to subfamilies, genera and species with brief species descriptions are provided; the ecology and ethology of individual species are discussed.

scholarly journals Recent changes in the frequency of plant species and vegetation types in Scania, S Sweden, compared to changes during the twentieth century

2019 ◽  
Vol 29 (3) ◽  
pp. 709-728 ◽  
Author(s):  
Torbjörn Tyler ◽  
Stefan Andersson ◽  
Lars Fröberg ◽  
Kjell-Arne Olsson ◽  
Åke Svensson ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Plant Species ◽  
Human Activities ◽  
Individual Species ◽  
Vegetation Types ◽  
Forest Species ◽  
Calcareous Fens ◽  
Grassland Species ◽  
Arable Weeds ◽  
Seminatural Grasslands

AbstractBased on data from three surveys of the vascular flora of the province of Scania, southernmost Sweden, conducted 1938–1971, 1987–2006 and 2008–2015, we analyse the change in frequency of individual species and groups of species associated with particular vegetation types. A majority of all species have experienced a change in frequency since 1938, and this turnover has continued in recent decades. The species showing the most dramatic declines since 1987 represent a mixture of arable weeds, grassland species and ruderals, but excludes forest species. In contrast, a majority of the most increasing species are escapes from cultivation that thrive under shaded conditions. The vegetation types showing the largest decreases since 1987 are all open seminatural grasslands and wetlands, while the vegetation types performing best are wooded. All vegetation types increasing since 1987 also increased during the 1900s; however, species of wooded types performed relatively better in recent decades, as opposed to the minimal increase observed for species of vegetation strongly influenced by human activities. Among decreasing vegetation types, those that have received much attention from conservationists, e.g. sand-steppe and calcareous fens tend to perform relatively better now than during the 1900s, while those that have received less attention, e.g. poor fens, oligotrophic waters and heaths, now comprise the most rapidly declining vegetation types. A majority of the species that decreased 1938–1996 also decreased 1987–2015, but, in general, species shown to have increased during the 1900s have not continued to increase.

scholarly journals Leaf Area Index identified as a major source of variability in modelled CO<sub>2</sub> fertilization

2018 ◽  
Author(s):  
Qianyu Li ◽  
Xingjie Lu ◽  
Yingping Wang ◽  
Xin Huang ◽  
Peter M. Cox ◽  
...  
Keyword(s):  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Vegetation Types ◽  
Cable Model ◽  
Co2 Fertilization ◽  
Area Index ◽  
Leaf Level

Abstract. The concentration-carbon feedback factor (β), also called the CO2 fertilization effect, is a key unknown in climate-carbon cycle projections. A better understanding of model mechanisms that govern terrestrial ecosystem responses to elevated CO2 is urgently needed to enable a more accurate prediction of future terrestrial carbon sink. We calculated CO2 fertilization effects at various hierarchical levels from leaf biochemical reaction, leaf photosynthesis, canopy gross primary production (GPP), net primary production (NPP), to ecosystem carbon storage (cpool), for seven C3 vegetation types in response to increasing CO2 under RCP 8.5 scenario, using the Community Atmosphere Biosphere Land Exchange model (CABLE). Our results show that coefficient of variation (CV) for the CABLE model among the seven vegetation types is 0.15–0.13 for the biochemical level β, 0.13–0.16 for the leaf-level β, 0.48 for the βGPP, 0.45 for the βNPP, and 0.58 for the βcpool. The low variation of the leaf-level β is consistent with a theoretical analysis that leaf photosynthetic sensitivity to increasing CO2 concentration is almost an invariant function. In CABLE, the major jump in CV of β values from leaf- to canopy- and ecosystem-levels results from divergence in modelled leaf area index (LAI) within and among the vegetation types. The correlations of βGPP, βNPP, or βcpool with βLAI are very high in CABLE. Overall, our results indicate that modelled LAI is a key factor causing the divergence in β values in CABLE model. It is therefore urgent to constrain processes that regulate LAI dynamics in order to better represent the response of ecosystem productivity to increasing CO2 in Earth System Models.

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