Preferential flow reduces water residence times and allows rapid transport of pollutants such as organic contaminants. Thus, preferential flow is considered to reduce the influence of soil matrix-solute interactions during solute transport. While this claim may be true when rainfall directly follows solute application, forcing rapid chemical and physical disequilibrium, it has been perpetuated as a general feature of solute transport—regardless of the magnitude preferential flow. A small number of studies have alternatively shown that preferential transport of strongly sorbing solutes is reduced when solutes have time to diffuse and equilibrate within the soil matrix. Here we expand this inference by allowing solute sorption equilibrium to occur and exploring how physiochemical properties affect solute transport across a vast range of preferential flow. We applied deuterium-labeled rainfall to field plots containing manure spiked with eight common antibiotics with a range of affinity for the soil after 7 days of equilibration with the soil matrix and quantified preferential flow and solute transport using 48 soil pore water samplers spread along a hillslope. Based on > 700 measurements, our data showed that solute transport to lysimeters was similar—regardless of antibiotic affinity for soil—when preferential flow represented less than 15% of the total water flow. When preferential flow exceeded 15%, however, concentrations were higher for compounds with relatively low affinity for soil. We provide evidence that (1) bypassing water flow can select for compounds that are more easily released from the soil matrix, and (2) this phenomenon becomes more evident as the magnitude of preferential flow increases. We argue that considering the natural spectrum preferential flow as an explanatory variable to gauge the influence of soil matrix-solute interactions may improve parsimonious transport models. Many of these compounds are susceptible to rapid movement via preferential flow4, which can often lead to orders of magnitude greater solute leaching than predicted by equations specific to a homogenous soil matrix 6. Preferential flow is ubiquitous in soils7, representing as little as 1%8 to more than 70% of total water movement11. The associated potential for rapid chemical transport threatens water quality in nearby aquifers and streams, making it important to understand the key physical and chemical factors controlling organic compound movement through unsaturated soils12.A growing and increasingly affluent human population is releasing ever greater loads of organic contaminants into Earth’s critical zone−113. Under such conditions, flow rates can sharply increase without uniform increases in soil pore water pressures15 as water bypasses the lower permeability matrix . This phenomenon is observed over a broad range of water contents18. In dry soils, preferential flow may occur as partially water-repellant layers destabilize the wetting front forming fingered flow17 or as flow is concentrated through newly-formed cracks20. As soils approach saturation, near-positive pore water pressures can force water from the matrix into highly conductive macropores24, making total flow proportionally more preferential25.Preferential flow occurs as a disequilibrium between water flowing through the low-permeability bulk soil  and the highly conductive fraction of the total soil volume such as macropores with hydraulic conductivities > 0.01 cm h26, thus limiting the opportunity for dissolved substances to sorb to soil particles28. Therefore, when preferential flow is minimal, more homogenous flow through the soil matrix dominates, favoring transport of compounds with low affinity to soil, such as those with a low sorption coefficient (Kd)31. Conventional considerations of preferential flow maintain that the influence of solute-matrix affinity decreases as flow becomes proportionally more preferential36 such that the kinetics of rapid flow restrict sorption37 or enhance desorption38. However, studies to date have mostly compared known preferential transport of solutes to more homogenous flow—either modeled based on advection–dispersion processes40 or focused on specific conditions such as frozen soils41—but have never directly assessed how organic contaminants of varying chemical properties become mobilized along a spectrum of flow heterogeneity. Relevant studies have primarily focused on precipitation-driven transport directly following solute application47, which—by forcing scenarios of rapid chemical and physical disequilibrium—may have perpetuated the view of preferential transport as a non-selective process . A small number of studies50 have noted that strongly sorbing solutes may be less susceptible to preferential leaching51 when rainfall timing is more lagged compared to solute application, facilitating more diffusion and sorption equilibrium within the soil matrix. Nevertheless, the conditions necessary to dampen versus amplify the influence of compound physiochemical properties on solute transport are not well understood. The primary objectives of this study were to (1) quantify transport of eight veterinary antibiotics under different preferential flow conditions and (2) determine if preferential flow can eliminate the influence of solute-soil affinity on transport of these solutes. This analysis is necessary to provide a fundamental understanding of how preferential flow alters contaminant mobility and build process-based transport models needed to manage water quality and thwart water resource degradation.Preferential flow drastically reduces the residence time of water in the critical zone53. and animal waste applied to soils can contain 40–90% of these compounds in unaltered (not metabolized) form54, and (2) for their wide range of affinity to soils55. Antibiotic-spiked manure was applied to field plots (200 × 150 cm) on the soil surface or injected to a depth of 10 cm (n = 3 plots per application method). After 7 days of rainfall suppression, we applied rainfall at 7 cm h−1  to these plots, as well as to an additional 3 plots without treated manure that served as experimental controls. Rainfall simulations were labeled with deuterium to facilitate preferential flow quantification. Simulations were conducted until 0.5 h of continuous runoff was observed56, and in total lasted for an average of 1.2 h : erythromycin (ERY), tylosin (TYL), tetracycline (TC), pirlimycin (PLY), chlortetracycline (CTC), oxytetracycline (OCT), sulfadimethazine (SDM), and sulfamethazine (SMZ). Veterinary antibiotics were chosen for (1) their environmental ubiquity, as up to 11.5 million kg were purchased in 2019 for livestock use alone in the U.S h Table . MonitorC, from samples collected 0.5 h into and 1 h after rainfall versus pre-event (background) values from the same lysimeter. We deemed ΔC to be zero whenever veterinary antibiotic concentrations decreased from background or were non-detectable. At the same time, we considered flow to be partitioned into two distinct hydrological domains assuming faster advection through preferential pathways  versus slower flow through the soil matrix via combined advection and dispersion mechanisms. Following the conceptual framework provided by Stumpp, et al.57, the fractional contribution of preferential flow was calculated by Dt(t), were used in a two-member mixing model that separated rainfall moving through preferential flow paths, DPF(t), from pre-event or mid-event soil matrix water, DMF(t)  see  would hafPF values, which we considered to represent entirely matrix-derived water (fPF = 0) in subsequent analyses. Though event water was applied at a constant rainfall intensity (7 cm h−1) and infiltrated in similar rates between plots  measurements (20%) resulted in zero or negative ts Table , simulatL−1 Fig. , with prL−1 Fig. . These nC > 0) showed clustering in three distinct ranges of preferential flow: 0 < fPF ≤ 0.15, 0.15 < fPF ≤ 0.35, and 0.35 < fPF ≤ 0.61 . The relatively low-affinity sulfonamides (SDM and SMZ) had a more uniform distribution across the range of preferential flow. While the high-affinity macrolides (ERY and PLY) had similar distributions as the sulfonamides, it should be noted that they were detected less often (N = 11 for ERY and 19 for PLY versus N = 22 for SDM and 35 for SMZ). Further, the sulfonamides were always detected under high preferential flow conditions , whereas ERY and PLY continued to have non-detects  in that range , the compounds with the greatest contrast in relative affinity to soil  were similar in ΔC for the low range of preferential flow, but diverged with increasing bypass flow  and highest (ERY and TYL) relative affinity using the data in Fig. C: 0.014 µg L−1 for sulfonamides versus 0.013 µg L−1 for macrolides when fPF was < 0.15. However, when preferential flow exceeded 0.35, ΔC was more than an order of magnitude higher for SDM compared to ERY. This finding suggests that the influence of solute-matrix affinity on transport was weakest when bypass flow was minimal.After binning data into the three preferential flow ranges . Some numerical simulations60 and one recent column study41 have also suggested that solutes with moderate affinity for soil may be most susceptible to preferential flow. Though the underlying mechanisms are not yet clear, we speculate that these compounds may have high enough Kd to be sorbed throughout the soil medium, yet soluble enough to be partitioned or displaced into local bypass flow. Thus, when fPF approached ~ 0.5 , compounds with moderate relative affinity for soil could be selected in higher proportions relative to other antibiotics.Here we note that the antibiotic PLY, which had a relatively moderate affinity for soil, produced the highest ΔPF Figs. b. HoweveC/fPF was highest) when fPF was < 0.15  and high (0.35 < fPF ≤ 0.61) fPF values, ΔC susceptibility to preferential flow was relatively constant. The influence of preferential flow on the magnitude of ΔC was therefore dampened when solute-matrix affinity became more influential, indicating a fundamental shift in solute and flow partitioning. For example, in a situation where ΔC linearly increased across the range of fPF values  across the spectrum of preferential flow. Instead, ΔC/fPF remained relatively constant above preferential flow values of 0.15 and ΔC was an order of magnitude more susceptible to preferential flow at the lowest versus highest fPF values. These data thus further illustrate that solute responses to bypass flow differ along the spectrum of preferential flow, and more specifically, that a shift from non-selective to selective transport coincided with a decrease in overall ΔC susceptibility to preferential flow at the length scale of the lysimeters.Solute transport to our lysimeters also appears to have been most susceptible to preferential flow , our fPF calculations would have detected fast-flowing event water contributions with the greatest potential to rapidly transport these solutes to depth. High ΔC at large values of fPF . Modeling results clearly predict that the difference in transport between solutes of high and low relative affinity decreased as the fraction of preferential flow increased . This approach meant we missed the opportunity to study precipitation-driven transport shortly after manure application, where the influence of compound properties would likely have been enhancedC range. Nonetheless, we did not observe any preference for compounds based on their relative affinity in this range, suggesting that these sources of uncertainty did not strongly influence our interpretation. We also calculated ΔC (and fPF) on a per-lysimeter basis rather than using a difference from a mean background level, thus avoiding uncertainty that would arise from a lumped field-scale metric . This not only supports our assumption of fPF = 0 for these samples but also suggests that susceptibility ΔC  the storm produced preferential flow estimates spanning 3 orders of magnitude, 2) the relative affinity of our eight antibiotics differed by up to two orders of magnitude, and 3) our analysis included > 700 measurements of antibiotic concentrations and preferential flow proportion. As a result, the conceptual framework developed in this study  may sufficiently describe solute movement under conditions of high, rather than minimal, preferential flow. At the same time, explicitly modifying the influence soil matrix-solute interactions along a spectrum of preferential flow may improve parsimonious transport models. Given the ubiquity of preferential flow observationsn = 3 plots) or injected below-ground into two 5 cm wide × 10 cm deep slits placed perpendicular to the slope and spanning the width of the plot frame and buffer strip . The three remaining plots were used as controls by avoiding manure application and input of antibiotics. However, we detected some residual antibiotics in the control plots, which likely remained from manure applications in previous years  with moderate soil structure. Soil physiochemical and hydraulic properties methods are described in Table ars Fig. . The prePrior to manure application, we installed a series of suction lysimeters  in the plot buffer strips to sample veterinary antibiotic transport in the subsurface. These buffer strips received the same amount of rainfall and manure treatment yet were located outside of the metal plot frames. Soil pore water samples were withdrawn from two randomly positioned lysimeters in both the Bt1 (30 cm) and Bt2 horizons (90 cm) to detect vertical movement of antibiotics in surface application plots, with two probes per depth making four probes per plot Figs.  and S10.−1 was used for all antibiotics. The spiked dairy manure slurry was then applied to each plot 7 days prior to rainfall simulations at a rate of 56 Mg wet mass ha−158. If natural rainfall occurred within the 7-day equilibration period, the plots were covered with plastic tarps to prevent unintentional water input to the plots58.A liquid slurry of dairy manure (5% solid content) was spiked with eight commonly used antibiotics: two macrolides, erythromycin and tylosin; two sulfonamides, sulfamethazine and sulfadimethoxine; three tetracyclines, oxytetracycline, chlortetracycline, and tetracycline; and one lincosamide, pirlimycin. A target concentration of 500 µg LKd, for each of the eight antibiotics with soil from the field site . Under this framework, each antibiotic was assigned a rank from 1 (highest affinity) to 8 (lowest affinity) based on measured Kd values  fitted to the sprinkler inlet. This system dispensed an enriched deuterium solution into the well water at a desired ratio of ~ 4:100 (deuterium-spiked water: well water). Discrete pore water samples were taken from all lysimeters by applying 60 kPa of suction for 10 min, with samples collected 1 h before the simulation, 0.5 h into the simulation, and 1 h after the simulation  and for all eight antibiotics via HPLC MS/MS, as detailed in the Supporting Information. To understand how these preferential flow estimates affected the transport of our eight veterinary antibiotics with a spectrum of relative affinity for soil, we quantified the change in concentration from lysimeter samples collected before versus during and after simulation (ΔC) as a function of fPF.After the 7-day equilibration period, we conducted rainfall simulations using deuterium-labeled well water to trace mobile infiltrating water and detect preferential flow contributions to pore water signature. The rainfall simulator (240 cm × 300 cm) followed the original design of Humphry, et al.ion Fig. . All liq88, reducing the likelihood of intercepting every preferential flow path below the plots. Suction cups can also have biased representation of water in larger—more “mobile”—pores91. We note that, though matrix and macropore waters can resist mixing during extreme rainfall92, complete mixing between pores can occur within days94. Thus, point measurements from our samplers  likely yielded representative samples of pre-event water from the matrix and labeled event water from mobile water in maropores, while capturing a wide range of stable isotope signatures.Here we note some potential constraints of using suction cup sampling to represent soil pore water. For example, suction lysimeters often have a smaller volume-of-influence compared to alternative pore water samplers57 the isotope mass balance can be described as:We considered flow to be partitioned into two distinct hydrological domains: faster advection through preferential pathways  and slower flow through the soil matrix via combined advection and dispersion mechanisms. Following the conceptual framework provided by Stumpp, et al.QPF(t), and matrix flow, QMF(t), equal total discharge Qt(t) [L3 T−1], and DMF(t), DPF(t), and Dt(t) [M L−3] correspond to the isotope concentrations within each flow component. Here DPF(t) was set equal to pre-event and mid-event isotope lysimeter signatures for samples taken 0.5 into and 1 h after rainfall, respectively. Assuming that preferential flow pathways translate to rainfall inputs during each sampling period, we consider the rainfall isotope signal to be equivalent to the preferential flow signal in the outlet95:And:Dt(t), detected in the outlet is equivalent to:The total isotope concentration, fMF(t) = 1 − fPF(t), Eq. (Since the fraction of matrix flow (t), Eq.  can be rfPF value of 0.50. Because event water reached the outlet before the wetting front it must have required preferential transport and thus 50% of the total water outflow is deemed preferential; with the remainder derived from pre-event matrix water.We also note that mass transfer between the slow flowing matrix water is implicitly considered in the mixing model. For example, we can consider a scenario where event water infiltrates into the soil matrix and spills into a preferential flow path yielding an By applying labeled rainfall simulations to a heterogeneous no-till soil containing manure spiked with 8 antibiotics, we were able to quantify the amount of preferential flow in lysimeter drainage and assess the influence of compound properties on solute transport using the compounds’ wide range of relative affinity to the matrix. Additionally, because (1) the mass of antibiotics applied in manure was consistent between compounds and manure treatment (surface application versus subsurface injection), (2) estimated half-lives (described above) suggest that degradation was a minimal over the 7-day equilibration period, and (3) we were not concerned with metabolites of these antibiotics, our analysis did not require the explicit use of reactive transport models.fPF was positive, such that we could observe when and if preferentially flowing water contained relevant (> background levels) levels of antibiotics. This filtering distills large sample numbers into the most relevant values, removing unnecessary variability in solute transport from analysis. Our analysis in Fig. fPF > 0. Thus, points where ΔC = 0 could bring down the observed average accordingly, giving a more representative depiction of how preferential flow contributed to the relative transport of antibiotics.Our analysis was primarily focused on conditions where C as function of preferential flow (fPF > 0 and ΔC > 0) by manure application treatment  and lysimeter depth (30 cm vs 90 cm). Specifically, this allowed us to identify the statistical significance of manure treatment on ΔC across the observed range of the covariate  while also testing for the interaction of depth on this relationship. The log-transformed data were found to meet ANOVA assumptions of normality  and homogeneity of variances (via Fligner’s test). We used R version 3.5.261 to conduct all statistical analyses with α = 0.05. We found no significant difference between the slope of lines fitted to ΔC data across the range of fPF for all treatments, and no significant influence of lysimeter depth on this relationship  there appears to be no consistent influence of sampling time on the relationship between ΔC and fPF . Thus, we compiled all data together for subsequent analyses without separating treatment or depth.We used a two-way analysis of covariance (ANCOVA) to statistically compare the slope of lines fitted to log-transformed Δld Table  suggestefPF Fig. , we consSupplementary Information 1.Supplementary Information 2.


NTR). p75NTR mediates both apoptosis and survival and its expression is increased in Alzheimer’s disease (AD) patients. Here, we investigate the importance of p75NTR for the cholinergic input to the hippocampus. Performing extracellular recordings in brain slices from p75NTR knockout mice (p75−/−) in presence of the muscarinic agonist carbachol, we find that gamma oscillation power and rhythmicity are increased compared to wild-type (WT) mice. Furthermore, gamma activity is more phase-locked to the underlying theta rhythm, which renders a stronger coupling of both rhythms. On the cellular level, we find that fast-spiking interneurons (FSNs) fire more synchronized to a preferred gamma phase in p75−/− mice. The excitatory input onto FSN is more rhythmic displaying a higher similarity with the concomitant gamma rhythm. Notably, the ablation of p75NTR counteracts the Aβ-induced degradation of gamma oscillations and its nesting within the underlying theta rhythm. Our results show that the lack of p75NTR signaling could promote stronger cholinergic modulation of the hippocampal gamma rhythm, suggesting an involvement of p75NTR in the downregulation of cognition-relevant hippocampal network dynamics in pathologies. Moreover, functional data provided here suggest p75NTR as a suitable target in the search for efficacious treatments to counteract the loss of cognitive function observed in amyloid-driven pathologies such as AD.Gamma and theta brain rhythms play important roles in cognition and their interaction can affect gamma oscillation features. Hippocampal theta oscillations depend on cholinergic and GABAergic input from the medial septum-diagonal band of Broca. These projecting neurons undergo degeneration during aging and maintain high levels of neurotrophin receptor p75 (p75 From the several oscillatory rhythms identified in the brain, two are intimately associated with cognitive tasks linked to the hippocampus, that is, learning and memory. These are the gamma (30–80 Hz) and theta (5–10 Hz) rhythms2, which can occur separately, but are also known to coexist and interact3. The resulting nesting of gamma oscillations within, or on top of, theta oscillations, also known as cross-frequency coupling, can greatly affect the characteristics of the gamma rhythm. Several interactions have been reported allowing a hierarchical organization of the rhythms that leads to precise neuronal firing patterns. The most common interaction that has been reported is the coupling between theta phase and gamma power10. Theta-phase to gamma-phase12 and theta power to gamma power couplings have also been described12.The synchronization of many of the different interconnected neuronal assemblies present in the hippocampal microcircuitry gives rise to the emergence of what is known as neuronal oscillations14. It is noteworthy that in addition to modulating gamma oscillations, the theta rhythm can also induce them15.Some physiological advantages of theta–gamma coupling are the possibility of encoding different information in different theta phases and the synchronization of neuronal ensembles over long distances16. Cholinergic fibers are known to innervate both pyramidal cells and interneurons in the hippocampus. Selective septo-hippocampal cholinergic lesions sparing GABAergic inputs can affect the amplitude of theta oscillations17. Thus, any factors that influence the development and/or survival of basal forebrain cholinergic neurons (BFCNs) are likely to have a profound impact on the dynamics of cognition-relevant oscillatory behavior in the hippocampus.Hippocampal theta rhythm depends partially on the cholinergic and GABAergic input from the medial septum and diagonal band of Broca (MS/DBB) located in the basal forebrain, which has been classically considered as the “pacemaker” for the hippocampal theta activity19. This age-dependent degeneration of BFCN might be due to a decrease in trophic support, as in aging an attenuation of the neurotrophic signaling in sensory neurons has been observed20. In hippocampal slices, it has been observed that selective ablation of the neurotrophin receptor TrkB in fast-spiking interneurons (FSNs) decreases the amplitude of carbachol (CCh)-induced gamma oscillations21. However, much less is known about how neurotrophic signaling may regulate neuronal network rhythms and paramount neuronal populations such as FSNs.Cholinergic neurons of the MS/DBB are known to undergo a moderate degeneration during aging resulting in a decrease of cholinergic function that has been related to the progressive memory deficits during aging24. The neurotrophin receptor p75 (p75NTR) has been reported to mediate both proapoptotic signaling and survival, as well as neurite growth25. BFCNs are among the few neuronal cell types that maintain p75NTR expression at high levels during adulthood. Van der Zee et al.26 first reported an increased number of cholinergic neurons in the septum of mice carrying a targeted mutation in the third exon of the p75NTR gene. Conversely, p75NTR overexpression significantly reduces dendritic complexity as well as spine density in CA1 pyramidal cells27. In addition, p75NTR interacts directly with amyloid-beta peptide (Aβ) mediating neuritic dystrophy29.On the other hand, neurotrophins are also known to affect the viability, differentiation, and size of cholinergic neuronsNTR in age-related neurodegenerative diseases like Alzheimer’s disease (AD). Although p75NTR has an evident effect on the overall cholinergic function in the brain, particularly on the survival and innervation of BFCN having the potential to modulate both hippocampal theta and gamma oscillations, the role of p75NTR on these oscillatory rhythms in normal and pathological conditions remains unknown. As such, it has been observed that Aβ induces dysfunction of glutamatergic neurons impairing septum rhythmicity. This may negatively affect hippocampal rhythmogenesis and underlie the memory loss observed in AD30.Such interaction points toward the deleterious effect of the presence of p75NTR ablation of increasing cognition-relevant hippocampal rhythms such as gamma oscillations, elucidate the underlying cellular mechanisms in FSN, and how it strengthens the interaction with the underlying theta oscillations in mouse brain slices. We show that lack of p75NTR signaling protects the hippocampal network against pathophysiological changes observed in AD such as the Aβ-induced degradation of gamma oscillations and gamma–theta interaction. It suggests p75NTR signaling inhibition as a potential focus for novel therapies against AD including cognitive-relevant rhythms strengthening.Here, we describe the effect of p75ExonIII+/− mice (p75+/−) with a C57BL/6J background were purchased from Jackson Laboratory (USA). Animals were inbred in order to obtain the p75+/+ and p75−/− mice used for experiments. The number of animals was reduced to a minimum by optimizing the use of slices from both hemispheres. The total number of animals was set to a minimum of 3 and a maximum of 5 per group and condition. Male animals between P15 and P21 were deeply anesthetized using isofluorane before being sacrificed by decapitation and brain removal.Experiments were performed in accordance with the ethical permit granted by Norra Stockholms Djurförsöksetiska Nämnd to AF (N45/13). p75p75com (5′-GGACAAACAGAACACAGTGTGTGA-3′) in exon III upstream of the insertion site, and primer p75wt (5′-ACCCATATAATCGCTGAGAGAGGA-3′) in the following intron for the detection of the wild-type allele  in the PGK promoter of the vector inserted26 for the detection of knockout allele  modified for dissection containing (in mM) 80 NaCl, 24 NaHCO3, 25 glucose, 1.25 NaH2PO4, 1 ascorbic acid, 3 Na pyruvate, 2.5 KCl, 4 MgCl2, 0.5 CaCl2, 75 sucrose, and bubbled with carbogen (95% O2 and 5% CO2). Horizontal sections (350 µm thick) of the ventral hippocampi of both hemispheres were prepared with a Leica VT1200S vibratome (Leica Microsystems). After cutting, slices were transferred into a humidified interface holding chamber containing standard ACSF (in mM): 124 NaCl, 30 NaHCO3, 10 glucose, 1.25 NaH2PO4, 3.5 KCl, 1.5 MgCl2, and 1.5 CaCl2, continuously supplied with humidified carbogen gas . The chamber was held at 37 °C during slicing and subsequently allowed to cool down to room temperature for at least 1 h before the commencement of experiments.Hippocampal slices were prepared as previously described+ gluconate, 8 KCl, 4 Mg2+ATP, 0.3 Na+GTP, 10 HEPES, 0.2 EGTA, 2 MgCl2 with pH set to 7.2–7.3, and osmolarity to 270–280 mosmol/l. AP firing was recorded in current clamp at free membrane potential.For local field potential (LFP) recordings glass microelectrodes (4–6 MΩ) filled with standard ACSF were placed in CA3 stratum pyramidale. Single-cell recordings were carried out in a submerged recording chamber. Action potentials (APs) and excitatory postsynaptic potentials (EPSCs) from FSNs in area CA3 were recorded in whole-cell patch-clamp mode with an internal recording solution containing (in mM): 122.5 K33 and location in the CA3 area. Interface chamber LFP recordings were performed with a 4-channel M102 amplifier . Data were sampled at 5 kHz, conditioned using a HumBug 50 Hz noise eliminator (Quest Scientific), low-pass filtered at 1 kHz, digitized , and stored on a hard drive using pCLAMP 9.6 software (Molecular Devices). Concomitant LFP and patch-clamp recordings in the submerged-type recording chamber were performed with a patch-clamp amplifier (Multiclamp 700B), and data were acquired using pCLAMP 10.4 software (Molecular Devices). LFP recordings were also conditioned using a HumBug 50 Hz noise eliminator (Quest Scientific). All signals recorded in submerged configuration were low-pass filtered at 1 kHz, acquired at 5 kHz, and digitized and stored using Digidata 1322A and pCLAMP 10.4 software .FSNs were visualized under an upright microscope using infrared-differential interference contrast (IR-DIC) microscopy  and were identified and distinguished from other interneuron populations based on their morphology and characteristic firing behavior Fig. 32,33 and34. Oscillations were allowed to stabilize for at least 20 min before recording.In all experiments, gamma oscillations were elicited by applying CCh (20 μM) to the extracellular bathγENV, see below) power was calculated as the integrated power between 5 and 10 Hz. Peak frequency for each signal was obtained as the frequency at which the maximum power of the respective frequency band was located. Once differences in the peak frequency were detected, further confirmation was derived from analyzing the lag to the second peak in the corresponding autocorrelogram (see below). For further analysis of LFP recordings, the signals were preprocessed using a band-pass Butterworth filter (in both directions to prevent a shift in the phase angle) set to 20–60 Hz for autocorrelation analysis of gamma oscillations and 4–10 Hz (theta) for gamma and theta interaction. Normalized autocorrelations were performed using Matlab custom-written routines. The coefficient of rhythmicity (Cr) was calculated from the autocorrelograms as a measure of the quality of gamma and theta oscillations. It was defined as Cr = (α − β)/(α + β) including a modification to Andersson et al.35: prior to Cr calculation, (1 + α) and (1 + β) corrections were applied with α corresponding to the value of the height of the second peak and β to the first trough in the autocorrelogram (counting the first peak at zero lag). Cr ranges between 0 and 1 with higher coefficient values denoting more rhythmic activity. Only recordings having a Cr ≥ 0.01 were considered rhythmic and included in the study36.Fast Fourier transformations for power spectra were calculated from 60-s-long LFP data traces (segments of 8192 points) using Axograph X software . Gamma oscillation power was calculated by integrating the power spectral density from 20 to 80 Hz using KaleidaGraph. Theta oscillation power was calculated on 4–10 Hz filtered traces. Gamma envelope (γENV) was set to “peak” mode and peak separation was set to 100 in order to finely adjust the envelope to the amplitude of the signal. In order to assess the reliability of the method, we have analyzed ten recordings from the control group (p75+/+) taken at random using different peak adjustment settings  and the γENV were used. Normalized XC between θ and the γENV ) was performed with a Matlab custom-written routine. The oscillatory nature of the signals was evidenced in the XC analysis that displayed several peaks with periodic fluctuations. To describe the similarity and phase shift of both signals, we considered the magnitude and lag of the central negative peak, respectively37.A gamma amplitude time series were obtained using a Matlab custom-written routine, where the built-in envelope function  and how recurrent the firing in that angle was (vector length). Thus, a larger vector denotes a more synchronized AP firing. The vector length is shown normalized by the total number of AP for each cell recorded from both p75+/+ and p75−/−. All distributions were tested for uniformity. Inclusion criteria were set at p < 0.05 value by performing a Rayleigh’s test.Spike-phase coupling analysis was performed on concomitant LFP recordings and single-cell recordings using a custom-made routine in Matlab to relate the FSN spiking activity to ongoing gamma oscillationsAll chemical compounds used in intracellular and extracellular solutions were obtained from Sigma-Aldrich Sweden AB . CCh was dissolved in milliQ water.38. Briefly, Aβ was expressed in BL21*(DE3) pLysS Escherichia coli and purified with DEAE-Sepharose . To get rid of large aggregates, the peptides were passed through a 30,000 Da Vivaspin concentrator  at 4 °C. The filtrate was concentrated at 4 °C with a 5000 Da Vivaspin concentrator  until Aβ concentration was ~50 µM. The peptides were aliquoted in low-bind Eppendorf tubes (Axygen) and stored at −20 °C.Met-Aβ residues 1–42 (referred to as Aβ herein) were produced as previously described40 as well as in submerged-type chambers36. Tests for statistical significance were performed on absolute values in all the experiments using two- or one-sided when appropriate. Student’s t test or Mann–Whitney U test were performed depending on the parametric or nonparametric distribution, respectively. Significance levels were set as follows: *p < 0.05, **p < 0.01, ***p < 0.005, and ****p < 0.0001.All statistical analysis was performed using GraphPad Prism. Results are reported as mean ± SEM. Prior statistical analysis all the data were subjected to an outliers determination and removal with the ROUT  method, followed by tests for normality distribution and variance similarity between groups. Due to previous experience with outliers and overall sample behavior, sample size was determined based on previous studies performed in interface-type chambersNTR signaling loss on cholinergic receptor-driven oscillatory rhythms in the murine hippocampus, we performed LFP recordings in the CA3 region of horizontal hippocampal slices. LFP gamma oscillations were elicited by applying 20 μM CCh to the bath solution and allowed to stabilize for no <20 min. CCh has been shown previously to be able to induce both gamma (30–80 Hz) and theta (5–10 Hz) oscillations in vitro44. In our experiments, we were able to record large and consistent gamma oscillations ). The XC displayed a prominent negative peak with a lag close to zero  in the p75+/+ mice indicating that peaks of γENV occur earlier in the theta cycle in p75−/− littermates  peak was also analyzed in order to determine the strength of the theta–gamma coordination. Compared to p75+/+, p75−/− mice showed a significant increase in the XC peak  in FSNs during ongoing gamma oscillations in order to study the effect of p75ass Fig. . We founncy Fig. . Consequ+/+ Fig. .−/−-EPSCs displayed a higher rhythmicity compared to p75+/+-EPSCs .It has been observed that AD patients and AD mouse models display alterations of cognition-relevant brain rhythm properties and dynamicsNTR ablation could be affected by the presence of Aβ. For this purpose, we recorded CCh-induced gamma oscillations from slices preincubated for 15 min with 50 nM Aβ. Similar to previous findings in WT mice33, the presence of Aβ led to a significant decrease of gamma oscillation power in p75+/+ mice , while p75−/− mice did not show any disruption of the theta rhythm following exposure to Aβ ) as measurements of the interaction strength and the phase locking of both signals, respectively37. Although Aβ did not induce a significant shift in the phase relationship between θ and γENV signals  peak of the signals recorded from p75+/+ was significantly decreased by Aβ exposure, while the XC peak of the signals recorded from p75−/− remained unchanged after Aβ exposure  or the fact that some studies use cocktails of receptor agonists and antagonists to elicit theta oscillations57, while others use whole hippocampal preparations to study spontaneously generated theta rhythms58.Theta oscillations have been reported to interact with and change the properties of the gamma rhythm, such as amplitude, phase, or power of the oscillation60.Nonetheless, even though our experiments were designed to favor the recording of gamma oscillations, we were able to obtain a small but consistent underlying theta rhythm that could be isolated by filtering the LFP signal in order to remove the dominant gamma oscillations  cycle by fast precisely tuned hyperpolarizations of the membrane potentialsee Fig. .37. Our data showed gamma–theta nesting in both mouse strains with better coupling of the rhythms in the absence of p75−/− signaling. Moreover, p75−/− mice displayed a larger-amplitude oscillation. This opens the possibility for the hippocampal microcircuitry to resonate without a larger external theta input, which could lead to an increase in gamma oscillation power via a mechanism similar to the one operating in the intact brain. Studies directed to dissect the modulation phenomena will further our understanding of the theta–gamma interactions observed here. It is known that the inputs from the GABAergic inhibitory neurons from the MS/DBB—regarded as the main theta input into the hippocampus—can greatly influence this modulation64. In line with the better synchronization with the gamma rhythm observed for the FSN firing behavior in p75−/− mice, it has been proposed that the amplitude modulation of the gamma rhythm at theta frequencies may arise from a phasic interruption of the interneuronal network activity, as the gamma power and interneuronal firing are both modulated at theta and are in phase with each other1.In addition, we also tested whether the theta-attuned gamma oscillations (here induced by CCh) also exhibit an oscillating amplitude peak patterned in the theta band as it occurs in vivo66, both gamma–theta phase locking/nesting and gamma oscillation strengthening in the presence of CCh strongly support the notion that the role of the cholinergic innervation of the hippocampal region—increased due to p75NTR ablation—is to facilitate the coordination between the gamma rhythms generated in the hippocampus and GABAergic-driven theta rhythms generated also in the MS/DBB.Taken together, and consistent with previous reports67.Normal neuronal synchrony underlies the generation of brain rhythms, such as gamma oscillations, that promote cognitive functions. There is increasing evidence that the disruption of these rhythms can be used as an early functional biomarker of AD since initial alterations in hippocampal network activity arise long before Aβ accumulation and plaque formation. Both disruption of brain rhythms and aberrant Aβ accumulation constitute the pathophysiological and histological hallmarks of the disease, respectively. Moreover, these disease features correlate with the progressive cognitive decline patients experience with the advance of AD to ever more severe stages. Supporting evidence comes from animal studies in which robust alterations of theta–gamma coupling in area CA1/subiculum of AD mouse models was observed before Aβ overproduction36. Consistent with previous findings, here we have observed that acute Aβ induced degradation of gamma oscillations and rhythmicity in p75+/+ mice. Theta oscillation power was also decreased, and a significant disruption of theta–gamma nesting was evident in area CA3 of p75+/+ hippocampal slices. It is noteworthy that the rhythmicity of theta oscillations also significantly decreased compared to p75−/− Aβ-treated slices. Given the drastic disruption of both rhythms and their interaction, it is tempting to speculate that the Aβ-induced shift of the gamma-preferred theta phase angle (see Fig. NTR signaling is intact.We have also observed that acute exposure of neuronal networks to Aβ induces firing desynchronization and degradation of hippocampal gamma oscillations in vitrosee Fig.  may be rNTR promotes Aβ neuritic dystrophy both in vivo and in vitro28, and is upregulated in the hippocampus of late-stage AD patients68. This upregulation of p75NTR expression in AD patients’ brain parallels the one observed in AD mouse models69. Synaptic loss is one of the features of neuronal damage in AD, and it strongly correlates with cognitive impairment in patients71. Interestingly, the presence of interfering molecules that potentially prevent Aβ–p75NTR interaction has been shown to have neuroprotective properties72. Moreover, age-dependent degeneration of BCNF is prevented by modulation of p75NTR 19.p75NTR ablation significantly strengthens and protects neuronal network dynamics from Aβ-induced neuronal network dysfunction. The p75−/− mouse strain used in our study lacks the extracellular receptor domain capable of interacting with extracellular ligands. As such, it displayed a gamma rhythm with increased power and increased rhythmicity, as well as better synchronized FSN activity. The latter appears to be the profound functional effect caused by the deletion of p75NTR signaling since a more synchronized FSN activity leads to the rescue of gamma oscillations during sharp waves ripples, decreases Aβ load, and induces a turnover of microglia from a pro-inflammatory phenotype towards a phagocytic state in the 5XFAD AD mouse model in vivo48. In addition, optogenetically driving medial septal FSNs restores hippocampal slow gamma oscillation amplitude as well as phase-amplitude coupling of the J20 AD mouse model73.Highly complementary to these previous studies at cellular and molecular levels, our functional findings reveal that p75NTR as a suitable target in the search for neuroprotective and/or neurorestorative strategies against AD that opens treatment avenues for other cognitive-compromised and amyloid-driven pathologies. Studies that include the blockade of p75NTR signaling as a therapeutic step during AD progression might help to counteract the degeneration of cholinergic neurons, altered neuronal network dynamics, and, hence, the cognitive dysfunction typical of AD.Finally, our study identifies p75Supplementary InformationSupplementary Figure 1Supplementary Figure 2Supplementary Figure 3Supplementary Figure 4Supplementary Figure 5Supplementary Figure 6Supplementary Table 1


We tested the effect of sampling location , general feeding strategy , phylogeny, and standard length on δ13C and δ15N. The reef fish communities of the artificial and natural reef tracts were significantly different; the artificial sites also exhibited more variability. For all samples, δ13C and δ15N ranged from -19.5 to -13.1‰ and 6.7–13.3‰, respectively. Significant effects were detected for both general feeding strategy and phylogeny. Significant differences were also seen in δ13C and δ15N profiles between artificial and natural reefs; however, these changes were primarily driven by differences in fish community structure, rather than by changes in the feeding strategy or trophic relationships of individual fish taxa. The trophic guild invertivore was the only group of fish to demonstrate significant isotopic differences between both reef tracts (inner and outer) and reef types . The artificial reef may act more as a foraging corridor between the natural first and second reef tracts for omnivores and carnivores. If the function of artificial reefs is to provide additional foraging habitat for fishes, then perhaps more time is needed for the trophically important, infaunal invertebrate community to develop similarly to the natural reef environment.Artificial reefs may enhance the biological production of reef-associated flora and fauna, but their trophic structure relative to that of natural reefs remains understudied. We assessed trophic relationships by 1) comparing reef fish communities and 2) comparing δ Reef fish, Stable isotopes, Artificial reef, Trophic dynamics In resp12/C13 and N14/N15 are most frequently used for trophic studies of marine fauna  between trophic steps  to study the trophic behavior of reef-associated fish is well-established ; however, we did detect significant differences in community species composition between natural and artificial reefs . Further analysis indicated that artificial reef communities were largely frequented by a subset of the overall community, most notably by Tomtate, Slippery Dick, Porkfish, Lionfish, Sharpnose Puffer, Purple Reef Fish, and French Grunt , nor did it affect species richness or evenness .Analysis of visual survey data revealed that artificial and natural reefs did not differ in their fish species richness , and reef subtype (inner vs. outer reef tract) on δ15N and δ13C values separately using ANOVA  and to a lesser extent, of reef type  but not by the artificial or natural state of the reef where it was collected – in other words, the stable isotope profiles of each fish taxon reflected their feeding ecology rather than reef type. We saw no evidence that fishes adjusted their feeding preference depending on the reef type from which they were sampled. Lastly, δ13C was significantly influenced by both trophic guild and by the distance from shore at which the individual was sampled, suggesting that there are differences between these communities  in their basal carbon sources.Our primary conclusion, based on comparative analysis of the catch and survey data, is that fish community composition differed significantly among reef types, and that this in turn shaped the overall stable isotope profiles at each location . Further3.1Species composition was significantly influenced by reef type, suggesting that the fish communities of the artificial reef sites and natural reef sites differed in their species composition. Previous studies in the region have shown that the fish assemblages of the first and second reef tracts are different . Additio3.215N and δ13C values of muscle tissue were primarily influenced by family and trophic guild. The results are also consistent with prior studies which found dietary habits directly affect δ15N and δ13C, and that higher trophic level feeding behavior results in enrichment of δ15N and δ13C  most closely resembles the δ13C of benthic marine algae, which makes it difficult to distinguish the two primary producers  was too depleted, so mangroves were excluded. Seagrasses were also considered as a possible food source, but the exhibited δ13C range (-13 to -7‰) was much more enriched than the herbivores collected in this study, with the exception of the Bicolor Damselfish. 15N of 5.6‰, which was too enriched to be the basal carbon source for the Bicolor Damselfish of this study. 13C by as much as 9‰. It may be that the complex structure of the artificial reef piles where the Bicolor Damselfish were sampled reduced water movement enough to cause the algal food source to become more enriched in δ13C.The calculated δ feeders . The avene algae ; this agroducers . It is u3.4omnivore were slightly more enriched in δ15N (9.3‰ ± 0.5) and more depleted in δ13C (-17.0‰ ± 0.85) when compared to those in the trophic guild herbivore  of trophic guild planktivore suggests that phytoplankton is the source of primary production in the diet of these species. Additionally, the δ15N-based trophic position estimates using phytoplankton as the food web base more closely matches the stomach content-based trophic position when compared to the other primary producers. Phytoplankton tends to exhibit δ15N that is less enriched when compared to marine benthic algae  relative to the other trophic guilds in this study, with the exception of the trophic guild carnivore, which is consistent with higher trophic level feeding habits relative to the other trophic guilds of this study. Species within the trophic guild invertivore are known to feed primarily on marine invertebrate fauna, and 15N-based trophic position estimates using marine benthic algae as the food web base were closest to the stomach content-based trophic positions.The trophic guild 3.6carnivore consists of reef-associated fish species that exhibit a diet of both marine invertebrates and teleost fishes. Piscivory (exclusive consumption of fishes) is associated with higher trophic level feeding, and it was expected for this reason that individuals within this guild would exhibit the highest levels of enrichment in δ15N (15N (10.7‰ ± 1.01), it is only slightly more enriched compared to the mean δ15N of the trophic guild invertivore (10.5‰ ± 0.74). Additionally, the mean δ13C of the trophic guild carnivore (15.5‰ ± 1.4) is similar to the mean δ13C of the trophic guild invertivore (15.1‰ ± 1.07), suggesting that the individuals of these two trophic guilds share similar feeding habits. The mean δ13C of species within this guild suggest that marine benthic algae are the major carbon source for their diets  and distance from shore (inner versus outer). The mean δ13C for these locations  increased slightly with seaward movement. This trend is consistent with prior studies that reported increasingly depleted δ13C values of sampled fauna with seaward movement and depth  had the lowest δThe stable isotopic trends observed in the reef fish communities of the artificial reef and the adjacent first and second reef tract suggest that the artificial reefs are acting as a corridor between the first and second reef, with opportunistic feeding occurring at the artificial reef sites. It is well established that connectivity between reef habitats is important for overall reef health and in this capacity, the artificial reefs studied here seem to increase connectivity and simultaneously provide opportunistic foraging habitat.4Fish community composition data collection was limited to two surveys completed within one year. During these surveys, two members of a three-person dive team identified 83 different fish species across 17 families. Spearguns were used to acquire fish samples for stable isotope analysis, which inherently limited the size range of the fishes that could be sampled.Christopher Blanar: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Joseph Hornbeck & David William Kerstetter: Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Amy Hirons: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.10.13039/100006596Florida Fish and Wildlife Conservation Commission (FWC-11229).This work was supported by Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


The productivity of permanent temperate cut grasslands is mainly driven by weather, soil characteristics, botanical composition and management. To adapt management to climate change, adjusting the cutting dates to reflect earlier onset of growth and expansion of the vegetation period is particularly important. Simulations of cut grassland productivity under climate change scenarios demands management settings to be dynamically derived from actual plant development rather than using static values derived from current management operations. This is even more important in the alpine region, where the predicted temperature increase is twice as high as compared to the global or Northern Hemispheric average.For this purpose, we developed a dynamic management module that provides timing of cutting and manuring events when running the biogeochemical model LandscapeDNDC. We derived the dynamic management rules from long-term harvest measurements and monitoring data collected at pre-alpine grassland sites located in S-Germany and belonging to the TERENO monitoring network. We applied the management module for simulations of two grassland sites covering the period 2011–2100 and driven by scenarios that reflect the two representative concentration pathways (RCP) 4.5 and 8.5 and evaluated yield developments of different management regimes.−1 yr−1 as required by national legislations. Our study also shows that yields significantly decreased in drought years, when soil moisture is limiting plant growth but due to comparable high precipitation and water holding capacity of soils, this was observed mainly in the RCP 8.5 scenario in the last decades of the century.The management module was able to represent timing of current management operations in high agreement with several years of field observations (r² > 0.88). Even more, the shift of the first cutting dates scaled to a +1 °C temperature increase simulated with the climate change scenarios (−9.1 to −17.1 days) compared well to the shift recorded by the German Weather Service (DWD) in the study area from 1991−2016 (−9.4 to −14.0 days). In total, the shift in cutting dates and expansion of the growing season resulted in 1−2 additional cuts per year until 2100. Thereby, climate change increased yields of up to 6 % and 15 % in the RCP 4.5 and 8.5 scenarios with highest increases mainly found for dynamically adapted grassland management going along with increasing fertilization rates. In contrast, no or only minor yield increases were associated with simulations restricted to fertilization rates of 170 kg N ha Our hypothesis is that pre-alpine grassland simulations with static management can lead to significantly lower yields than dynamic management simulations, and that reduced rates of N fertilization will result in lower yields particularly under climate change conditions.Therefore, we present in this study a new dynamic management approach that we implemented in the biogeochemical model LandscapeDNDC , which d22.1The new dynamic management module implemented into LandscapeDNDC  is situated in an alpine valley at 864 m.a.s.l. and is characterized by a mean annual temperature (MAT) of 6.9 °C and a mean annual precipitation (MAP) of 1347 mm. The low elevation site Fendt  is situated in the foothills of the Alps at 595 m.a.s.l. with 8.9 °C MAT and 956 mm MAP . The soiThe vegetation in Graswang is dominated by species communities of Festuca pratensis Huds., Poa pratensis L., Prunella vulgaris L., Plantago lanceolate L., Knautia arvensis (L.) J.M. Coult., Pimpinella major (L.) Huds., and Trifolium repens L, but also includes species preferring wetter conditions, like Bistorta officinalis Delarbre and Polygonum bistorta L.. Species such as Arrhenatherum elatius (L.) P. Beauv. ex J. Presl & C. Presl, Festuca rubra L., Lolium perenne L., P. lanceolata, P. vulgaris, Ranunculus repens L., T. repens, and Veronica chamaedrys L. are characteristic for the Fendt site, along with Carum carvi L., F. pratensis, Pimpinella saxifrage L., P. pratensis, and Taraxacum officinale F.H. Wigg which are dominant only at Fendt .−1 for Graswang and 11.2 ± 2.4 t DM ha−1 for Fendt as derived from replicated (N = 3) biomass harvests from lysimeters covering an area of 1 m². For more details on lysimeter operation see e.g. Both grassland sites were subject to intensive management operations, equal to 4–5 cuts and 4–5 slurry applications per year following real local farmers practice in the pre-alpine study region. Mean yearly (2012−2018) yields were 10.4 ± 1.6 t DM ha2.2x  is a general plant physiology model for different types of crops and grass species that runs on top of a photosynthesis model after x distinguishes the four plant compartments leaf, stem, roots and storage. Leaves and stems represent aboveground plant tissue directly promoting growth and structure. Storage represents an empirical bulk compartment of all compounds that do not directly support growth and structure at a given time but can be mobilized e.g., during regrowth after cutting and in spring θstorΔGDD Eq.  and the 2 into roots γcut=xm and species-specific allocation parameters:The share of the remaining assimilated carbon between leaf and stem compartment Eq.  is deterCarbon that has been allocated to the storage is translocated to other plant organs after defoliation events, e.g., cutting or grazing and at the onset of the vegetation period. At such events, all carbon from the storage is distributed according to current allocation factors.demandN) at each time step is then given by NdemaT and a species-specific critical temperature Leaf biomass and a species-specific parameter describing specific leaf area (sco) Eq. :(8)arubiught Eq. , temperaught Eq.  and nitrught Eq.  on photoGPP), which is provided by the photosynthesis model after x:Assimilated carbon via photosynthesis is partly metabolized by growth and maintenance respiration. Growth respiration Rg  Eq.  is given10 temperature dependency with increasing temperature.Growth respiration is assigned to the specific compartments depending on the current biomass allocation fraction tion Eq.  for all fficient :(13)Rm.xNon-respiratory plant carbon losses include root exudation and plant senescence. Root exudation is given as a fraction related to root respiration Eq. :(14)Rm.xPlant senescence Eq.  is givenught Eq. , frost (ught Eq.  and planught Eq. :(15)Sx=mT in the air and the soil for above- and belowground senescence, respectively.These response functions are:2.2.4x model simulates soil carbon and nitrogen turnover and the associated processes humification, mineralization, nitrification, denitrification and ammonia volatilisation  for plant uptake and microbial driven production and emissions of C (CO2) and N  emissions as well as other loses such as NO3 leaching and NH3 emissions. In addition to substrate availability , all microbial processes depend on soil moisture and soil temperature, which are provided by above-described sub-models as well as the model input quantities pH and soil texture.The MeTrlisation . These p2.3For grassland simulations, the LandscapeDNDC management module requires inputs for execution of cutting and manuring events and further information on quantity and composition of the applied manure (see Section 2.3.1−1) and respective cutting dates  following actual farmers’ practice in the study region. These data were used to fit a linear regression to maximum standing biomass versus time, which allows to define a “target biomass” for executing a cutting event for any DOY. Hence, in the dynamic management model a cut is scheduled if the target biomass at a given DOY exceeds the threshold given by the regression equation (The dynamic management model was developed from long term field data (2012–2016) of a total of 22 biomass harvests (N = 3)  of intensively (4–5 cuts) used grasslands depending on elevation (h in m.a.s.l.)  directly derived from field data (.l.) Eq.  as deriv.l.) Eq. .(19)AGB=We compared results from this function considering respective elevations of the three study sites Graswang, Rottenbuch and Fendt and found only minor deviations of −1.8 % to −7.1 % from the field measurements.1.)If the target biomass of the first cutting event is not reached after DOY 150, the first cut will be set at DOY 151.2.)If the target biomass for all following cutting events is not reached within 55 days, they will be set at DOY 56 after the previous cutting event.If the target biomass is not reached after a given time (day of the year: DOY), further rules are considered see also , which a1.)st of February.The first manure event is scheduled at the start of the growing season as simulated by the vegetation sub-model but only at times without snow cover or frozen soil. Due to national legislation  manure e2.)−1 yr−1 (All other manure applications are scheduled within 7 days after respective cutting events on the first day without heavy rain (< 5 mm). Note that due to regional farmers practice and according to recommendations from extension services no manure is applied after the second cut. In line with legislation driven limitation of fertilization rates to ≤ 170 kg N ha−1 yr−1  for the Since timing of manure events is highly related to timing of cutting events, we defined the following rules regarding manure applications:2.3.2First, we examined the capability of the site-specific and the general regression model to reproduce the field data management at Graswang and Fendt. To do so we split the available data into a calibration (2012–2014) and a validation (2015–2018) period and ran simulations with weather data from on-site climate stations , the C:N ratio and if available information on the partitioning of nitrogen in fractions of NH4+, NO3, UREA and dissolved organic nitrogen (DON). For our study, information on cutting and manuring dates and quantities were available for the time period 2012−2018. Slurry composition was derived from analysis of slurry samples  of each fertilization event from 2012 to 2016. Mean slurry carbon and nitrogen loads and pH values were 437 ± 130 kg C ha−1 and 48 ± 10 kg N ha−1 and 7.6 ± 0.4, respectively. Given this information on grassland management, which is often not available in this detail (i)−1 yr−1);for an overall evaluation of LandscapeDNDC grassland biomass predictions (2012–2018) we used real time dates of single cutting and manuring events and measurements of manure composition −1 yr−1);for climate change scenario simulations (2011–2100) with static management settings we used mean cutting and manuring dates of 2012–2018 −1 yr−1) and a scenario with reduced nitrogen fertilization (≤ 170 kg N ha−1 yr−1) following changes in legislation in 2018  with dynamic management we derived cutting and manure events on the fly of simulations with the dynamic management module for a scenario with previously common fertilization rates  and iii) manure characteristics were represented by means of measurements of 2012 to 2016. For the limited nitrogen scenario only, we slightly adjusted total carbon and nitrogen loads per event to achieve a maximum of 170 kg N ha2.4.2LandscapeDNDC allows a flexible vertical parameterization of the soil profile, depending on available measurements. LandscapeDNDC was mainly developed and validated for single species setups (mainly crops in arable systems) rather than for simulating complex plant communities e.g. characterized by multiple plant functional types, a main feature of many grassland ecosystems. Therefore, we simulated grass growth still by the single species approach but in our case growth parameters represent mean values (see Table S2) which originate from the calibration to the plant mixtures  and TMIN Graswang = 0.993 * TMIN Garmisch – 1.3523 (r² = 0.91).Since climate stations in Graswang and Fendt have only been operated since 2012, LARSWG calculations were informed instead by weather data from longer observation records of nearby stations of the German Weather Service (DWD). For Fendt, precipitation and air temperature data were taken from 17 years (2000–2017) time series of the DWD station Wielenbach  with a MAP of 968 mm (Fendt site: 956 mm), minimum MAT of 3.56 °C (Fendt site: 3.54 °C) and a maximum MAT of 14.68 °C (Fendt site: 14.32 °C). For Graswang, precipitation was derived from a 15 years time series (2002–2017) of the DWD rainfall station Ettal-Graswang  with a MAP of 1545 mm which is reasonable higher (+ 198 mm) than MAP measured on site. For the Graswang site air temperature was taken from the DWD station Garmisch-Partenkirchen  but due to systematic differences in MAT  these data were corrected using a linear regression of Graswang and Garmisch air temperature data for the years 2012 to 2016: TGlobal radiation was taken for both sites from DWD station Hohenpeißenberg .Within the RCP 4.5 scenario a mean annual temperature increase within the vegetation period of maximum 1.4 °C is predicted from 2011 to 2070 and from thereon less steep by up to 1.7 °C in the year 2100 . For RCP2 concentrations to a fixed value of 400 ppm, while transiently (yearly) increasing atmospheric CO2 concentrations were used for the climate change scenarios based on the datasets provided by 2 in 2100 in RCP 4.5 and RCP 8.5 scenarios, respectively.For all simulations under current climate conditions we set atmospheric CO2.52) as well as the concordance correlation coefficient (CCC)  . Root meFor tests on normality of the empirical distribution for any parameter, we used the Shapiro-Wilk test. In case of normal distributed data, we assessed correlation using the Pearson correlation coefficient. For non-normally distributed data, the Spearman rank test was used.All statistical analysis and figures were generated using SAS/STAT software, Version 9.4 of the SAS System for Windows. Copyright © 2012−2018 SAS Institute Inc. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc., Cary, NC, USA.33.1Robust simulations of grassland biomass development and yields at respective cutting events are essential for the applicability of LandscapeDNDC for evaluation of grassland functions under current and climate change conditions. −1 (NRMSE = 38.6 %). Considering the complete simulation period of seven years with a total of 32 cutting events resulted in RMSE of 720 and 917 kg DW ha−1 and r² of 0.61 (p < .0001) and 0.52 (p < .0001) for Graswang and Fendt, respectively .Disregarding the year 2013 with exceptional high measured biomass for the first two cuts in Graswang and Fendt and the first cutting event in Fendt in 2014, patterns and magnitude of the simulated biomasses were mostly consistent with measurements. Statistical measures of the calibration (2012–2014) and validation (2015–2018) period were in the same range  with the only exception in the calibration period for Fendt with a higher RMSE value of 1127 kg DW ha3.2st to 5th cuts were not significantly different from measured values  but due to error propagation deviations of simulations and field measurements increased with increasing number of cuts , namely Nesselwang, Memmingen and Unterhausen. For the start of the growing season b a stron3.33.3.1As the validation results for the reference period did not show any significant differences in model performance between the site-specific and the general dynamic management approach, we present here only data of the general approach. In both RCP scenarios with progression of time a clear trend towards an earlier simulated start of the growing season and first cutting events are evident . Simulat3.3.2th and 75th percentiles) towards earlier first cuts between two periods 1991−2000 and 2007–2016 was observed. Referencing this to the mean temperature increase in the same period of +0.48 °C results in an earlier timing of the first cut between 9.4–14.0 days per 1 °C temperature increase.Compilation of data of the start of the growing season and first cut from >50 sites of the phenological observation network of the German Weather Service (DWD) located in the pre-Alpine study region revealed a significant trend towards earlier dates of first cuts from 1991 to 2016 , following the trend of increasing mean annual air temperatures during this time period  . A shiftResults of the RCP climate scenario simulations of LandscapeDNDC for an equally long period (2011−2040) agreed well with these observations with a similar range of 9.1–16.9 days earlier first cutting dates referenced to a temperature increase of 1 °C .Table 5SIn contrast to the shifts observed for first cutting dates, the DWD phenological observations do not show a clear trend of changes in the timing of the start of the growing season  with med3.3.3Trends towards an earlier start of the growing season and first cutting dates as simulated by the dynamic management routine of LandscapeDNDC influenced also the total number of cutting events per year. For the > 200 kg N dynamic simulations the number of cuts increased at both sites and in both RCPs from alternating between four and five cuts (2011−2035) to regularly five cuts after 2035. For the RCP 8.5 scenario from 2080 onwards, even six cuts were simulated at the warmer Fendt site and after 2090 likewise for the colder Graswang site. Within the reduced N scenarios, four cuts were constantly simulated for both sites between 2011 and 2035 and a slower increase to a maximum of five cuts thereafter. Five cuts were continuously simulated from 2045 at the earliest for Fendt RCP 8.5 and from 2080 at the latest for Graswang RCP 4.5 without a predicted increase towards six cutting events.3.3.4−1 as required by legislation since 2018.The previous findings of dynamic grassland management simulations showed that climate change beside earlier execution of the first cut result in increasing number of cuts and associated manure events, features which cannot be reflected by static management or if annual fertilization rates are restricted to 170 kg N ha−1 yr−1 as compared to the higher loads of N fertilization under static and the dynamic management. Within RCP 8.5 simulations, the yield differences between the static and the reduced N management decreased in the 2071−2100 period (< 500 kg DW ha−1 yr−1) while the difference to the dynamic non-reduced N scenario increased to 2159 kg DW ha−1 yr−1. Overall, climate change induced increases of yields of the three management scenarios were about 500 kg DW ha−1 yr−1 between the period of 2011−2040 and 2071−2100 in RCP 4.5 and 8.5, except for the dynamic management and the RCP 8.5 scenario where yield increases for the same period of time with 1600 kg DW ha−1 yr−1 were much higher  between the static and dynamic reduced N scenario were simulated for Fendt in the RCP 4.5 and RCP 8.5 scenario for the period 2011−2040 which further decreased in the period 2071−2100. In contrast to Graswang, climate change as predicted by RCP 4.5 did not lead to increasing grassland biomass under static and the dynamic reduced N management, while for the dynamic non-reduced N management increases of about 650 kg DW ha−1 yr−1 were predicted during both periods 2011−2040 and 2071−2100. In the RCP 8.5 scenario grassland yield increases under dynamic non-reduced N management at Fendt were similar to Graswang. This was not the case for the static and dynamic reduced N management which both showed even a decreasing trend from 2060 onwards. The yield increase in RCP 8.5 for the dynamic non-reduced N management resulted in a mean biomass of 11606 kg DW ha−1 yr−1 for the 2071−2100 period, which is about 1170 kg higher as compared to the start of the simulation period (2011−2040) and about 2000 kg DW ha−1 yr−1 higher than the mean biomass associated with static (9642 kg DW ha−1 yr−1) and dynamic non-reduced N (9557 kg DW ha−1 yr−1) management operations for 2071−2100.As compared to Graswang lower differences  DWD observations of the day of first cut at three independent grassland sites in the pre-alpine region of South Germany revealed a robust transferability of the general approach to larger areas with comparable site and climate conditions. This would include larger pre-alpine regions in Austria and Switzerland. The robustness was represented by an average difference between simulated and observed cuts of 2.2 ± 7.5 days, even though detailed data on biomass, soil and vegetation were missing for those sites. Nevertheless, more thorough testing with additional field data will be necessary to further evaluate the transferability of the model to other sites and regions of concern.The ability of LandscapeDNDC to simulate biomass yields in good agreement with field measurements was essential for the implementation and application of the dynamic management approach, which automatically executes grassland cuts if the simulated biomass equals a defined target biomass. The target biomass was in our case best represented, successively determined during calibration, by a linear regression of the 75The stronger systematic bias between the simulated and observed start of the growing season for the three DWD sites might be attributed to different definitions of this event. While criteria of observed dates are rather subjective and represented by 25 % of the grassland showing fresh green leaves irrespective of the species composition, the start of the growing season in the LandscapeDNDC model is strictly defined by a growing degree-day threshold.st cut) or latest 55 days after the previous cutting event, whenever the target biomass is not reached. In implementing this rule, we considered not only physiological but also fodder quality aspects, because local farmers limit the time between single cuts to avoid loss of fodder quality due to too long ageing of the sward. In view of climate change and an earlier start of the growing season, the now strictly defined latest DOY for the first cut and the fixed days between cuts could be adapted by setting time limits for cutting events after certain days without further grassland growth  . Howeverd summer . A more d summer , where cOur approach is fully dynamic and calculates cutting events on the fly of biogeochemical simulations without the need of file-based changes of harvesting schemes as applied by 4.2−1 yr−1 had no impact on the DOY of the first cut, overall lower number of cutting events associated with reduced nitrogen fertilization rates slowed down regrowth which resulted in longer periods between single cutting events and lower yields as compared to the non-reduced fertilization scenario.Climate change shifts the beginning of the vegetation period, allowing for earlier first cutting dates. Mean absolute changes over all ten different climate realizations between the period 2011−2040 and 2071−2100 were 18 ± 1.17 days and 37 ± 1.6 days for the RCP 4.5 (+1.7 °C) and RCP 8.5 (+4.4 °C) scenario, respectively, with minor differences between the Fendt and Graswang sites. This resulted in increasing number of cuts, from four to five under present conditions, to constantly five cutting events per year after 2035, and even six in the RCP 8.5 scenario after 2080 at both sites. Based on the DWD phenological data we were able to show that the observed rates of shifted first cutting events related to the observed air temperature increase agreed very well with simulations . While tOverall, the simulated shifts of the first cutting event compares well with other observations. th of March) in the high-emissions scenario RCP 8.5 appear debatable, particularly because they entail radiation intensities that are low for supporting plant growth  were simulated with the dynamic management without nitrogen reduction particularly in the RCP 8.5 scenario at the end of the simulation period. This resulted from both, higher yields at respective cutting events and increased number of cuts per year. In contrast to th cut (∼ 250 kg DM ha−1 yr−1) were less important at our study sites (−1 yr−1) than in the RCP 4.5 scenario (up to 650 kg DW ha−1 yr−1). These values compare well with increases of yields reported in other grassland simulation studies based on GDD based management approaches, with yield differences between static and dynamic management in the same order of magnitude  or even non evident (Fendt) with static management. This is in line with −1 yr−1), particularly towards the end of the RCP 8.5 simulation period, while annual fertilization rates in the static management remain lower at 190 kg N ha−1 yr−1. Yields of the dynamic reduced nitrogen fertilization scenario (maximum of 170 kg ha−1 yr−1) were in a comparable range than those of static management and thus significantly lower than simulated with the dynamic non-reduced nitrogen management. This shows that potential yield increases under climate change conditions can only be achieved if also manure application rates are adjusted. This is in line with −1 yr−1 (2O (−1 yr−1 were rather low. Thus, careful intensification of grassland management to support increased yields under climate change at suitable fields could allow for reducing management intensity at other fields supporting e.g. biodiversity and likely allowing improved ecosystem service provision on farm and regional scale.Within our dynamic management approach without nitrogen reduction, increasing number of cuts also led to increasing number of manure events, thus higher loads of N fertilization (up to 280 kg N ha −1 yr−1 . As repoyr−1 (2O  and nitryr−1 (2O  even wit5Our study demonstrates the importance of dynamic rules for adapting management activities to changing environmental conditions in the context of model-based assessments of climate change impacts on grassland productivity. This finding highly supports the statement of Krischan Petersen, David Kraus, Pierluigi Calanca contributed to further development of the model; Krischan Petersen and Mikhail A. Semenov developed the climate change scenario data; Krischan Petersen, Pierluigi Calanca and Ralf Kiese planned and carried out the simulations; Krischan Petersen and Ralf Kiese took the lead in writing the manuscript with input from all authors. All authors provided critical feedback and helped shape the research, analysis and manuscript. Krischan Petersen and Klaus Butterbach-Bahl conceived the study and were in charge of overall direction and planning.The authors report no declarations of interest.