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Our growing understanding of the plant tree of life provides a novel opportunity to uncover the major drivers of angiosperm diversity. Using a time-calibrated phylogeny, we characterized hot and cold spots of lineage diversification across the angiosperm tree of life by modeling evolutionary diversification using stepwise AIC (MEDUSA). We also tested the whole-genome duplication (WGD) radiation lag-time model, which postulates that increases in diversification tend to lag behind established WGD events. Diversification rates have been incredibly heterogeneous throughout the evolutionary history of angiosperms and reveal a pattern of ‘nested radiations’ – increases in net diversification nested within other radiations. This pattern in turn generates a negative relationship between clade age and diversity across both families and orders. We suggest that stochastically changing diversification rates across the phylogeny explain these patterns. Finally, we demonstrate significant statistical support for the WGD radiation lag-time model. Across angiosperms, nested shifts in diversification led to an overall increasing rate of net diversification and declining relative extinction rates through time. These diversification shifts are only rarely perfectly associated with WGD events, but commonly follow them after a lag period.

This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time series with lag time up to 20 days for all five wells. The results from S1 prove that the ensemble EDL generally performs superior to the DL in the estimation of GWL of each station using data of remaining four wells except the Paya Indah Wetland in which the DL method provide better estimates compared to EDL. Regarding S2, the EDL also exhibits superior performance in predicting daily GWL in all five stations compared to the DL model. Implementing EDL decreased the RMSE, NAE and RRMSE by 11.6%, 27.3% and 22.3% and increased the R, Spearman rho and Kendall tau by 0.4%, 1.1% and 3.5%, respectively. Moreover, EDL for S2 shows a high level of precision within less time lag, ranging between 2 and 4 compared to DL. Therefore, the EDL model has the potential in managing the sustainability of groundwater in Malaysia.

In multivariate time series systems, it has been observed that certain groups of variables partially lead the evolution of the system, while other variables follow this evolution with a time delay; the result is a lead–lag structure amongst the time series variables. In this paper, we propose a method for the detection of lead–lag clusters of time series in multivariate systems. We demonstrate that the web of pairwise lead–lag relationships between time series can be helpfully construed as a directed network, for which there exist suitable algorithms for the detection of pairs of lead–lag clusters with high pairwise imbalance. Within our framework, we consider a number of choices for the pairwise lead–lag metric and directed network clustering model components. Our framework is validated on both a synthetic generative model for multivariate lead–lag time series systems and daily real-world US equity prices data. We showcase that our method is able to detect statistically significant lead–lag clusters in the US equity market. We study the nature of these clusters in the context of the empirical finance literature on lead–lag relations, and demonstrate how these can be used for the construction of predictive financial signals.

Unprecedented decreases in atmospheric nitrogen (N) deposition together with increases in agricultural N-use efficiency have led to decreases in net anthropogenic N inputs in many eastern US and Canadian watersheds as well as in Europe. Despite such decreases, N concentrations in streams and rivers continue to increase, and problems of coastal eutrophication remain acute. Such a mismatch between N inputs and outputs can arise due to legacy N accumulation and subsequent lag times between implementation of conservation measures and improvements in water quality. In the present study, we quantified such lag times by pairing long-term N input trajectories with stream nitrate concentration data for 16 nested subwatersheds in a 6800 km2, Southern Ontario watershed. Our results show significant nonlinearity between N inputs and outputs, with a strong hysteresis effect indicative of decadal-scale lag times. The mean annual lag time was found to be 24.5 years, with lags varying seasonally, likely due to differences in N-delivery pathways. Lag times were found to be negatively correlated with both tile drainage and watershed slope, with tile drainage being a dominant control in fall and watershed slope being significant during the spring snowmelt period. Quantification of such lags will be crucial to policy-makers as they struggle to set appropriate goals for water quality improvement in human-impacted watersheds.

Climate‐vegetation interaction assessments often focus on vegetation response to concurrent climatic perturbations, seldom on the time‐lag effect of climate. Here we employ global satellite observations, climate data records and CO2 flux measurements to calculate the time‐lag of vegetation response to climate. We analyze the time‐lags of various climate variables under distinct environmental conditions to gain insight into how the long‐term climatic regimes and tree cover influence the time‐lag effects. Our findings reveal that terrestrial ecosystems characterized by arid and cold climates show more concurrent climate‐vegetation interactions than other ecosystems. Whereas areas with higher tree cover and humid ecosystems with both high mean annual temperature and precipitation show substantial time‐lag response of vegetation to climate by up to 6 months. Since the global climate‐vegetation interaction is dominated by time‐lag effects, incorporating these effects is paramount to improve our understanding of vegetation dynamics under a changing climate. Plain Language Summary When studying how climate affects vegetation, many studies usually focus on immediate plant responses, without considering the long‐term effects of climate. In our study, we used satellite data to look at how plant photosynthesis and growth changed over time in response to concurrent and past climates. We found that in dry and cold areas, plants respond quickly to changes in climate. But in regions with high tree cover and humid climate, plant responses to climate can take up to 6 months. Understanding these delays is crucial for predicting how vegetation will respond as the climate changes around the world. Key Points Terrestrial ecosystems with higher tree cover respond to climate perturbation more slowly than grasslands and croplands Temperature consistently has more significant impacts on vegetation in the longer term than VPD and soil moisture Arid and cold ecosystems show shorter time‐lag responses of vegetation to climate

Watershed and global-scale nitrogen (N) budgets indicate that the majority of the N surplus in anthropogenic landscapes does not reach the coastal oceans. While there is general consensus that this 'missing' N either exits the landscape via denitrification or is retained within watersheds as nitrate or organic N, the relative magnitudes of these pools and fluxes are subject to considerable uncertainty. Our study, for the first time, provides direct, large-scale evidence of N accumulation in the root zones of agricultural soils that may account for much of the 'missing N' identified in mass balance studies. We analyzed long-term soil data (1957-2010) from 2069 sites throughout the Mississippi River Basin (MRB) to reveal N accumulation in cropland of 25-70 kg ha−1 yr−1, a total of 3.8 1.8 Mt yr−1 at the watershed scale. We then developed a simple modeling framework to capture N depletion and accumulation dynamics under intensive agriculture. Using the model, we show that the observed accumulation of soil organic N (SON) in the MRB over a 30 year period (142 Tg N) would lead to a biogeochemical lag time of 35 years for 99% of legacy SON, even with complete cessation of fertilizer application. By demonstrating that agricultural soils can act as a net N sink, the present work makes a critical contribution towards the closing of watershed N budgets.

Abstract Multiple rounds of whole-genome duplication (WGD) followed by diploidization have occurred throughout the evolutionary history of angiosperms. Much work has been done to model the genomic consequences and evolutionary significance of WGD. While researchers have historically modeled polyploids as either allopolyploids or autopolyploids, the variety of natural polyploids span a continuum of differentiation across multiple parameters, such as the extent of polysomic versus disomic inheritance, and the degree of genetic differentiation between the ancestral lineages. Here we present a forward-time polyploid genome evolution simulator called SpecKS. SpecKS models polyploid speciation as originating from a 2D continuum, whose dimensions account for both the level of genetic differentiation between the ancestral parental genomes, as well the time lag between ancestral speciation and their subsequent reunion in the derived polyploid. Using extensive simulations, we demonstrate that changes in initial conditions along either dimension of the 2D continuum deterministically affect the shape of the Ks histogram. Our findings indicate that the error in the common method of estimating WGD time from the Ks histogram peak scales with the degree of allopolyploidy, and we present an alternative, accurate estimation method that is independent of the degree of allopolyploidy. Lastly, we use SpecKS to derive tests that infer both the lag time between parental divergence and WGD time, and the diversity of the ancestral species, from an input Ks histogram. We apply the latter test to transcriptomic data from over 200 species across the plant kingdom, the results of which are concordant with the prevailing theory that the majority of angiosperm lineages are derived from diverse parental genomes and may be of allopolyploid origin.

1. Species distribution shifts in response to climate change require that recruitment increase beyond current range boundaries. For trees with long life spans, the importance of climate-sensitive seedling establishment to the pace of range shifts has not been demonstrated quantitatively. 2. Using spatially explicit, stochastic population models combined with data from long-term forest surveys, we explored whether the climate-sensitivity of recruitment observed in climate manipulation experiments was sufficient to alter populations and elevation ranges of two widely distributed, high-elevation North American conifers. 3. Empirically observed, warming-driven declines in recruitment led to rapid modelled population declines at the low-elevation, 'warm edge' of subalpine forest and slow emergence of populations beyond the high-elevation, 'cool edge'. Because population declines in the forest occurred much faster than population emergence in the alpine, we observed range contraction for both species. For Engelmann spruce, this contraction was permanent over the modelled time horizon, even in the presence of increased moisture. For limber pine, lower sensitivity to warming may facilitate persistence at low elevations — especially in the presence of increased moisture — and rapid establishment above tree line, and, ultimately, expansion into the alpine. 4. Synthesis. Assuming 21st century warming and no additional moisture, population dynamics in high-elevation forests led to transient range contractions for limber pine and potentially permanent range contractions for Engelmann spruce. Thus, limitations to seedling recruitment with warming can constrain the pace of subalpine tree range shifts.

Aims Quantitative characterization of the time-lag effect between canopy temperature and atmospheric temperature and its controlling factors in the agricultural ecosystem may contribute to a higher inversion accuracy of soil water content using canopy-air temperature information. Methods Tc of winter wheat were continuously monitored, and the data of such environmental factors as solar radiation (Rs), atmospheric temperature (Ta), relative humidity (RH) and soil water content (SWC) were simultaneously collected. Results Hysteresis existed between Tc and Ta over the diel cycles, and different weather and irrigation levels did not change the direction of the time lag loop. the key driver regulating the diel hysteresis pattern between Tc and Ta varied under different weather: on rainy days, key driver was Rs while on cloudy and sunny days, the key driver was RH. the multiple regression model indicated that together Rs, Ta, RH, and SWC explained 58 ± 10% of the variation of time-lag effect. Path analysis showed on rainy days the key driver (Rs and RH) could enhance the time-lag effect through other indirect factors (Ta and SWC); on cloud days the key driver (RH and SWC) could inhibit the time-lag effect through other indirect factors (Ta); On sunny days this mutual inhibition was further significant. Conclusions These findings indicated a dynamic process of time-lag effect between Tc and Ta with different weather and different irrigation levels. This study contributes to the understanding of the time-lag effect and its driving factors and this analysis provides the basis for further improvement in monitoring crop water deficit.

Vegetation growth and carbon fixation in terrestrial ecosystems are seriously affected by climate. It is especially true in the Arctic due to the amplifying climate warming. This study aims to comprehend the response mechanism of the Arctic gross primary productivity (GPP) to different climatic factors such as temperature, precipitation, and fractional snow cover (FSC). First, the distribution and interannual variation of meteorological factors in the past two decades were investigated. Then, their contributions to GPP variation and dependence on elevation, latitude, and vegetation types were analyzed at different temporal scales. Finally, the time-lag effects of GPP response to meteorological factors were also explored on a monthly scale. The results show that temperature has the strongest impact on GPP, FSC follows, and precipitation ranks last. The positive response of GPP to temperature does not show time lag effects in most areas, and the negative response to precipitation displays time lags in approximately half of the Arctic regions. For FSC, the positive effects on GPP present more than 2 months lags, while the negative effects show at most a 1-month lag. The response of GPP to meteorological factors shows dependence on temporal scales. The change of GPP on the monthly scale mainly depends on the spatial distribution of these meteorological factors. On the annual scale, GPP is also affected by the interannual change of these meteorological factors. This study holds important significance in enhancing our understanding of how the carbon cycle within the Arctic terrestrial ecosystem responds to climate change.