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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.

Aim Biodiversity responses to changing environmental forcing on species are often characterized by considerable time-lags (= relaxation times). Although changes to the occurrence and abundance of species likely have cascading effects (e.g. on species of other trophic levels, genes, community structure and ecosystem processes), current concepts addressing lagged biodiversity responses are limited to single drivers affecting a few biodiversity components (e.g. extinction debt in terms of species numbers or population size). Little attention has been paid to the interacting and cumulative nature of time-lag phenomena. Here, we synthesize current knowledge, mechanisms and implications of delayed biodiversity responses and propose a 'cumulative biodiversity lags-framework' which aims to integrate lagged responses of various components of biological organization. Location Global. Results Effects of change in environmental forcing are transmitted along a series of linked cause–effect relationships which act on different biodiversity components (e.g. individuals, populations, species, communities). We show that lagged responses to environmental forcing are caused by different mechanisms (e.g. metapopulation dynamics, dispersal limitation, successional dynamics), which operate sequentially on these intermediary links. Lags manifest themselves on the respective biodiversity component which changes over time; the full relaxation time of a focal system will therefore depend on the aggregate length of different lags. We elucidate key mechanisms and circumstances which are likely to cause cumulative lagged responses, and propose research avenues to improve understanding of cumulative biodiversity lags. Main conclusions The failure to give adequate consideration to widespread cumulative time-lags often masks the full extent of biodiversity changes that have already been triggered. Effects that are particularly relevant for human livelihoods (e.g. changes in the provision of ecosystem services) may emerge with the most pronounced delay. Accordingly, the consideration of appropriate temporal scales should become a key topic in future work at the science–policy interface.

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.

We investigate deviations in the mean phase travel time of acoustic waves preceding the emergence of 46 large active regions observed by the Helioseismic and Magnetic Imager. In our investigation, we consider two different procedures for obtaining the mean phase travel time, by minimizing the difference between cross-correlations and a reference, as well as the Gabor wavelet fitting procedure. We cross-correlate the time series of mean phase travel time deviations with the surface magnetic field and determine the peak correlation time lag. We also compute the perturbation index—the area integrated mean phase travel time deviations exceeding quiet Sun thresholds—and compare the time of peak perturbation index with the correlation time lag. We find that the lag times derived from the difference minimization procedure precede the flux emergence for 36 of the 46 active regions, and that this lag time has a noticeable correlation with the maximum flux rate. However, only 28 of the active regions have peak perturbation index times in the range of 24–48 hr prior to the flux emergence. Additionally, we examine the relationship between the properties of the emerged active regions and the strength of helioseismic signals prior to their emergence.

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.

Context Landscape habitat amount is known to increase biodiversity, while the effects of habitat fragmentation are still debated. It has been suggested that negative fragmentation effects may occur with a time lag, which could explain inconsistent results. However, there is so far no empirical support for this idea. Objectives We evaluated whether habitat amount and fragmentation at the landscape scale affect the species density of deadwood-dwelling lichens, and whether these effects occur with a time lag. Methods We surveyed deadwood-dwelling lichens in woodland key habitats in two regions in northern Sweden, and modelled their species density as a function of past (1960s) and present (2010s) habitat amount (old forest area) and fragmentation (edge density) in the surrounding landscapes. Results Present habitat amount generally had weak positive effects on lichen species density. Positive effects of the past habitat amount were stronger, indicating a time lag in habitat amount effects. Habitat fragmentation effects were generally weak and similar whether fragmentation was measured in the past or the present landscapes, indicating no time lag in fragmentation effects. Conclusions We found a time lag effect of habitat amount, but not fragmentation. This result is not consistent with suggestions that time lags explain the mixed observations of fragmentation effects. Time-lag effects of habitat amount suggest that the studied lichen communities face an extinction debt. Conservation should therefore prioritize increasing the amount of old forest, for example by creating forest reserves, to maintain the current lichen diversity. More generally, our results imply that studies examining only the present habitat amount risk under-estimating its importance.

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

Water quality has suffered as humans have increased nutrient inputs across the landscape. In many cases, management actions to reduce nutrient inputs have not been met with concomitant ecosystem responses. These missed expectations are partly due to the continued slow delivery of nutrient-enriched groundwater pre-dating input reductions resulting from management actions. Land use legacies as expressed through this time lag are important to quantify in order to adjust management expectations. We present a novel coupling of nitrogen source maps with groundwater transport times to create a high-resolution (120 m) fully distributed estimate of the timing and magnitude of groundwater nitrogen deliveries to surface water across Michigan's Lower Peninsula. This new view of the landscape has been designed around common management timelines for: elected officials looking to make a difference for re-election (<5 years), career managers hoping to see the fruits of their labor (5-30 years), and advocacy groups whose work can span generations (>30 years). One striking result is that after 100 years, in our study area, approximately 50% of the nitrogen that enters the groundwater system remains in transit. This means that actions taken now may not show the expected lower nitrogen loads to receiving ecosystems for decades to centuries. We show that differences in groundwater travel times create a heterogeneous patchwork over which managers can prioritize actions to best match their targeted response times. Across the highest nitrogen inputs in our study region, less than 10% had short enough groundwater legacies to match the management timeline of most government and agency work. Agricultural practices (manure and chemical fertilizer) are the main nitrogen contributors across the top three management classes; however, human contributions through septic tank effluent and lawn fertilizers contribute 5%-8% of nitrogen.