Recovery from disturbance requires resynchronization of ecosystem nutrient cycles

Nitrogen (N) and phosphorus (P) are tightly cycled in most terrestrial ecosystems, with plant uptake more than 10 times higher than the rate of supply from deposition and weathering. This near-total dependence on recycled nutrients and the stoichiometric constraints on resource use by plants and microbes mean that the two cycles have to be synchronized such that the ratio of N:P in plant uptake, litterfall, and net mineralization are nearly the same. Disturbance can disrupt this synchronization if there is a disproportionate loss of one nutrient relative to the other. We model the resynchronization of N and P cycles following harvest of a northern hardwood forest. In our simulations, nutrient loss in the harvest is small relative to postharvest losses. The low N:P ratio of harvest residue results in a preferential release of P and retention of N. The P release is in excess of plant requirements and P is lost from the active ecosystem cycle through secondary mineral formation and leaching early in succession. Because external P inputs are small, the resynchronization of the N and P cycles later in succession is achieved by a commensurate loss of N. Through succession, the ecosystem undergoes alternating periods of N limitation, then P limitation, and eventually co-limitation as the two cycles resynchronize. However, our simulations indicate that the overall rate and extent of recovery is limited by P unless a mechanism exists either to prevent the P loss early in succession (e.g., P sequestration not stoichiometrically constrained by N) or to increase the P supply to the ecosystem later in succession (e.g., biologically enhanced weathering). Our model provides a heuristic perspective from which to assess the resynchronization among tightly cycled nutrients and the effect of that resynchronization on recovery of ecosystems from disturbance

Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter

Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Applications 20 (2010): 1285–1301, doi:10.1890/09-0876.1.Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter performance, but it did not improve performance when used individually. The EnKF estimates of leaf area followed the expected springtime canopy phenology. However, there were also diel fluctuations in the leaf-area estimates; these are a clear indication of a model deficiency possibly related to vapor pressure effects on canopy conductance.This material is based upon work supported by the U.S. National Science Foundation under grants OPP-0352897, DEB-0423385, DEB-0439620, DEB-0444592, and OPP- 0632139

Time lags: insights from the U.S. Long Term Ecological Research Network

Ecosystems across the United States are changing in complex ways that are difficult to predict. Coordinated long-term research and analysis are required to assess how these changes will affect a diverse array of ecosystem services. This paper is part of a series that is a product of a synthesis effort of the U.S. National Science Foundation’s Long Term Ecological Research (LTER) network. This effort revealed that each LTER site had at least one compelling scientific case study about “what their site would look like” in 50 or 100 yr. As the site results were prepared, themes emerged, and the case studies were grouped into separate papers along five themes: state change, connectivity, resilience, time lags, and cascading effects and compiled into this special issue. This paper addresses the time lags theme with five examples from diverse biomes including tundra (Arctic), coastal upwelling (California Current Ecosystem), montane forests (Coweeta), and Everglades freshwater and coastal wetlands (Florida Coastal Everglades) LTER sites. Its objective is to demonstrate the importance of different types of time lags, in different kinds of ecosystems, as drivers of ecosystem structure and function and how these can effectively be addressed with long-term studies. The concept that slow, interactive, compounded changes can have dramatic effects on ecosystem structure, function, services, and future scenarios is apparent in many systems, but they are difficult to quantify and predict. The case studies presented here illustrate the expanding scope of thinking about time lags within the LTER network and beyond. Specifically, they examine what variables are best indicators of lagged changes in arctic tundra, how progressive ocean warming can have profound effects on zooplankton and phytoplankton in waters off the California coast, how a series of species changes over many decades can affect Eastern deciduous forests, and how infrequent, extreme cold spells and storms can have enduring effects on fish populations and wetland vegetation along the Southeast coast and the Gulf of Mexico. The case studies highlight the need for a diverse set of LTER (and other research networks) sites to sort out the multiple components of time lag effects in ecosystems

N and P constrain C in ecosystems under climate change: role of nutrient redistribution, accumulation, and stoichiometry

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Rastetter, E., Kwiatkowski, B., Kicklighter, D., Plotkin, A., Genet, H., Nippert, J., O’Keefe, K., Perakis, S., Porder, S., Roley, S., Ruess, R., Thompson, J., Wieder, W., Wilcox, K., & Yanai, R. N and P constrain C in ecosystems under climate change: role of nutrient redistribution, accumulation, and stoichiometry. Ecological Applications, (2022): e2684, https://doi.org/10.1002/eap.2684.We use the Multiple Element Limitation (MEL) model to examine responses of 12 ecosystems to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in precipitation. Ecosystems respond synergistically to elevated CO2, warming, and decreased precipitation combined because higher water-use efficiency with elevated CO2 and higher fertility with warming compensate for responses to drought. Response to elevated CO2, warming, and increased precipitation combined is additive. We analyze changes in ecosystem carbon (C) based on four nitrogen (N) and four phosphorus (P) attribution factors: (1) changes in total ecosystem N and P, (2) changes in N and P distribution between vegetation and soil, (3) changes in vegetation C:N and C:P ratios, and (4) changes in soil C:N and C:P ratios. In the combined CO2 and climate change simulations, all ecosystems gain C. The contributions of these four attribution factors to changes in ecosystem C storage varies among ecosystems because of differences in the initial distributions of N and P between vegetation and soil and the openness of the ecosystem N and P cycles. The net transfer of N and P from soil to vegetation dominates the C response of forests. For tundra and grasslands, the C gain is also associated with increased soil C:N and C:P. In ecosystems with symbiotic N fixation, C gains resulted from N accumulation. Because of differences in N versus P cycle openness and the distribution of organic matter between vegetation and soil, changes in the N and P attribution factors do not always parallel one another. Differences among ecosystems in C-nutrient interactions and the amount of woody biomass interact to shape ecosystem C sequestration under simulated global change. We suggest that future studies quantify the openness of the N and P cycles and changes in the distribution of C, N, and P among ecosystem components, which currently limit understanding of nutrient effects on C sequestration and responses to elevated CO2 and climate change.This material is based on work supported by the National Science Foundation under Grant No. 1651722 as well through the NSF LTER Program 1637459, 2220863 (ARC), 1637686 (NWT), 1832042 (KBS), 2025849 (KNZ), 1636476 (BNZ), 1637685 (HBR), 1832210 (HFR), 2025755 (AND). We also acknowledge NSF grants 1637653 and 1754126 (INCyTE RCN), and DOE grant DESC0019037. We also acknowledge support through the USDA Forest Service Hubbard Brook Experimental Forest, North Woodstock, New Hampshie (USDA NIFA 2019-67019-29464) and Pacific Northwest Research Station, Corvallis, Oregon

The effects of CO2, climate and land-use on terrestrial carbon balance, 1920-1992: An analysis with four process-based ecosystem models

The concurrent effects of increasing atmospheric CO2 concentration, climate variability, and cropland establishment and abandonment on terrestrial carbon storage between 1920 and 1992 were assessed using a standard simulation protocol with four process-based terrestrial biosphere models. Over the long-term(1920–1992), the simulations yielded a time history of terrestrial uptake that is consistent (within the uncertainty) with a long-term analysis based on ice core and atmospheric CO2 data. Up to 1958, three of four analyses indicated a net release of carbon from terrestrial ecosystems to the atmosphere caused by cropland establishment. After 1958, all analyses indicate a net uptake of carbon by terrestrial ecosystems, primarily because of the physiological effects of rapidly rising atmospheric CO2. During the 1980s the simulations indicate that terrestrial ecosystems stored between 0.3 and 1.5 Pg C yr−1, which is within the uncertainty of analysis based on CO2 and O2 budgets. Three of the four models indicated (in accordance with O2 evidence) that the tropics were approximately neutral while a net sink existed in ecosystems north of the tropics. Although all of the models agree that the long-term effect of climate on carbon storage has been small relative to the effects of increasing atmospheric CO2 and land use, the models disagree as to whether climate variability and change in the twentieth century has promoted carbon storage or release. Simulated interannual variability from 1958 generally reproduced the El Niño/Southern Oscillation (ENSO)-scale variability in the atmospheric CO2 increase, but there were substantial differences in the magnitude of interannual variability simulated by the models. The analysis of the ability of the models to simulate the changing amplitude of the seasonal cycle of atmospheric CO2 suggested that the observed trend may be a consequence of CO2 effects, climate variability, land use changes, or a combination of these effects. The next steps for improving the process-based simulation of historical terrestrial carbon include (1) the transfer of insight gained from stand-level process studies to improve the sensitivity of simulated carbon storage responses to changes in CO2 and climate, (2) improvements in the data sets used to drive the models so that they incorporate the timing, extent, and types of major disturbances, (3) the enhancement of the models so that they consider major crop types and management schemes, (4) development of data sets that identify the spatial extent of major crop types and management schemes through time, and (5) the consideration of the effects of anthropogenic nitrogen deposition. The evaluation of the performance of the models in the context of a more complete consideration of the factors influencing historical terrestrial carbon dynamics is important for reducing uncertainties in representing the role of terrestrial ecosystems in future projections of the Earth system

Estimating uncertainty in ecosystem budget calculations

© The Authors, 2010. This article is distributed under the terms of the Creative Commons Attribution-Noncommercial License. The definitive version was published in Ecosystems 13 (2010): 239-248, doi:10.1007/s10021-010-9315-8.Ecosystem nutrient budgets often report values for pools and fluxes without any indication of uncertainty, which makes it difficult to evaluate the significance of findings or make comparisons across systems. We present an example, implemented in Excel, of a Monte Carlo approach to estimating error in calculating the N content of vegetation at the Hubbard Brook Experimental Forest in New Hampshire. The total N content of trees was estimated at 847 kg ha−1 with an uncertainty of 8%, expressed as the standard deviation divided by the mean (the coefficient of variation). The individual sources of uncertainty were as follows: uncertainty in allometric equations (5%), uncertainty in tissue N concentrations (3%), uncertainty due to plot variability (6%, based on a sample of 15 plots of 0.05 ha), and uncertainty due to tree diameter measurement error (0.02%). In addition to allowing estimation of uncertainty in budget estimates, this approach can be used to assess which measurements should be improved to reduce uncertainty in the calculated values. This exercise was possible because the uncertainty in the parameters and equations that we used was made available by previous researchers. It is important to provide the error statistics with regression results if they are to be used in later calculations; archiving the data makes resampling analyses possible for future researchers. When conducted using a Monte Carlo framework, the analysis of uncertainty in complex calculations does not have to be difficult and should be standard practice when constructing ecosystem budgets

Intra-tumor heterogeneity of MLH1 promoter methylation revealed by deep single molecule bisulfite sequencing

A single tumor may contain cells with different somatic mutations. By characterizing this genetic heterogeneity within tumors, advances have been made in the prognosis, treatment and understanding of tumorigenesis. In contrast, the extent of epigenetic intra-tumor heterogeneity and how it influences tumor biology is under-explored. We have characterized epigenetic heterogeneity within individual tumors using next-generation sequencing. We used deep single molecule bisulfite sequencing and sample-specific DNA barcodes to determine the spectrum of MLH1 promoter methylation across an average of 1000 molecules in each of 33 individual samples in parallel, including endometrial cancer, matched blood and normal endometrium. This first glimpse, deep into each tumor, revealed unexpectedly heterogeneous patterns of methylation at the MLH1 promoter within a subset of endometrial tumors. This high-resolution analysis allowed us to measure the clonality of methylation in individual tumors and gain insight into the accumulation of aberrant promoter methylation on both alleles during tumorigenesis

MGMT gene promoter methylation correlates with tolerance of temozolomide treatment in melanoma but not with clinical outcome

BACKGROUND: Despite limited clinical efficacy, treatment with dacarbazine or temozolomide (TMZ) remains the standard therapy for metastatic melanoma. In glioblastoma, promoter methylation of the counteracting DNA repair enzyme O(6)-methylguanine-DNA-methyltransferase (MGMT) correlates with survival of patients exposed to TMZ in combination with radiotherapy. For melanoma, data are limited and controversial. METHODS: Biopsy samples from 122 patients with metastatic melanoma being treated with TMZ in two multicenter studies of the Dermatologic Cooperative Oncology Group were investigated for MGMT promoter methylation. We used the COBRA (combined bisulphite restriction analysis) technique to determine aberrant methylation of CpG islands in small amounts of genomic DNA isolated from paraffin-embedded tissue sections. To detect aberrant methylation, bisulphite-treated DNA was amplified by PCR, enzyme restricted, and visualised by gel electrophoresis. RESULTS: Correlation with clinical data from 117 evaluable patients in a best-response evaluation indicated no statistically significant association between MGMT promoter methylation status and response. A methylated MGMT promoter was observed in 34.8% of responders and 23.4% of non-responders (P=0.29). In addition, no survival advantage for patients with a methylated MGMT promoter was detectable (P=0.79). Interestingly, we found a significant correlation between MGMT methylation and tolerance of therapy. Patients with a methylated MGMT promoter had more severe adverse events, requiring more TMZ dose reductions or discontinuations (P=0.007; OR 2.7 (95% CI: 1.32-5.7)). Analysis of MGMT promoter methylation comparing primaries and different metastases over the clinical course revealed no statistical difference (P=0.49). CONCLUSIONS: In advanced melanoma MGMT promoter, methylation correlates with tolerance of therapy, but not with clinical outcome

Abstraction in ecology : reductionism and holism as complementary heuristics

In addition to their core explanatory and predictive assumptions, scientific models include simplifying assumptions, which function as idealizations, approximations, and abstractions. There are methods to investigate whether simplifying assumptions bias the results of models, such as robustness analyses. However, the equally important issue - the focus of this paper - has received less attention, namely, what are the methodological and epistemic strengths and limitations associated with different simplifying assumptions. I concentrate on one type of simplifying assumption, the use of mega parameters as abstractions in ecological models. First, I argue that there are two kinds of mega parameters qua abstractions, sufficient parameters and aggregative parameters, which have gone unnoticed in the literature. The two are associated with different heuristics, holism and reductionism, which many view as incompatible. Second, I will provide a different analysis of abstractions and the associated heuristics than previous authors. Reductionism and holism and the accompanying abstractions have different methodological and epistemic functions, strengths, and limitations, and the heuristics should be viewed as providing complementary research perspectives of cognitively limited beings. This is then, third, used as a premise to argue for epistemic and methodological pluralism in theoretical ecology. Finally, the presented taxonomy of abstractions is used to comment on the current debate whether mechanistic accounts of explanation are compatible with the use of abstractions. This debate has suffered from an abstract discussion of abstractions. With a better taxonomy of abstractions the debate can be resolved.Peer reviewe

Homogeneous MGMT Immunoreactivity Correlates with an Unmethylated MGMT Promoter Status in Brain Metastases of Various Solid Tumors

The O6-methylguanine-methyltransferase (MGMT) promoter methylation status is a predictive parameter for the response of malignant gliomas to alkylating agents such as temozolomide. First clinical reports on treating brain metastases with temozolomide describe varying effects. This may be due to the fact that MGMT promoter methylation of brain metastases has not yet been explored in depth. Therefore, we assessed MGMT promoter methylation of various brain metastases including those derived from lung (n = 91), breast (n = 72) kidney (n = 49) and from malignant melanomas (n = 113) by methylation-specific polymerase chain reaction (MS-PCR) and MGMT immunoreactivity. Fifty-nine of 199 brain metastases (29.6%) revealed a methylated MGMT promoter. The methylation rate was the highest in brain metastases derived from lung carcinomas (46.5%) followed by those from breast carcinoma (28.8%), malignant melanoma (24.7%) and from renal carcinoma (20%). A significant correlation of homogeneous MGMT-immunoreactivity (>95% MGMT positive tumor cells) and an unmethylated MGMT promoter was found. Promoter methylation was detected in 26 of 61 (43%) tumors lacking MGMT immunoreactivity, in 17 of 63 (27%) metastases with heterogeneous MGMT expression, but only in 5 of 54 brain metastases (9%) showing a homogeneous MGMT immunoreactivity. Our results demonstrate that a significant number of brain metastases reveal a methylated MGMT-promoter. Based on an obvious correlation between homogeneous MGMT immunoreactivity and unmethylated MGMT promoter, we hypothesize that immunohistochemistry for MGMT may be a helpful diagnostic tool to identify those tumors that probably will not benefit from the use of alkylating agents. The discrepancy between promoter methylation and a lack of MGMT immunoreactivity argues for assessing MGMT promoter methylation both by immunohistochemical as well as by molecular approaches for diagnostic purposes