Sensitivity analysis as an aid in modelling and control of (poorly-defined) ecological systems

A literature review of the use of sensitivity analyses in modelling nonlinear, ill-defined systems, such as ecological interactions is presented. Discussions of previous work, and a proposed scheme for generalized sensitivity analysis applicable to ill-defined systems are included. This scheme considers classes of mathematical models, problem-defining behavior, analysis procedures (especially the use of Monte-Carlo methods), sensitivity ranking of parameters, and extension to control system design

Potential net primary productivity in South America: application of a global model

We use a mechanistically based ecosystem simulation model to describe and analyze the spatial and temporal patterns of terrestrial net primary productivity (NPP) in South America. The Terrestrial Ecosystem Model (TEM) is designed to predict major carbon and nitrogen fluxes and pool sizes in terrestrial ecosystems at continental to global scales. Information from intensively studies field sites is used in combination with continental—scale information on climate, soils, and vegetation to estimate NPP in each of 5888 non—wetland, 0.5° latitude °0.5° longitude grid cells in South America, at monthly time steps. Preliminary analyses are presented for the scenario of natural vegetation throughout the continent, as a prelude to evaluating human impacts on terrestrial NPP. The potential annual NPP of South America is estimated to be 12.5 Pg/yr of carbon (26.3 Pg/yr of organic matter) in a non—wetland area of 17.0 ° 106 km2. More than 50% of this production occurs in the tropical and subtropical evergreen forest region. Six independent model runs, each based on an independently derived set of model parameters, generated mean annual NPP estimates for the tropical evergreen forest region ranging from 900 to 1510 g°m—2°yr—1 of carbon, with an overall mean of 1170 g°m—2°yr—1. Coefficients of variation in estimated annual NPP averaged 20% for any specific location in the evergreen forests, which is probably within the confidence limits of extant NPP measurements. Predicted rates of mean annual NPP in other types of vegetation ranged from 95 g°m—2°yr—1 in arid shrublands to 930 g°m@?yr—1 in savannas, and were within the ranges measured in empirical studies. The spatial distribution of predicted NPP was directly compared with estimates made using the Miami mode of Lieth (1975). Overall, TEM predictions were °10% lower than those of the Miami model, but the two models agreed closely on the spatial patterns of NPP in south America. Unlike previous models, however, TEM estimates NPP monthly, allowing for the evaluation of seasonal phenomena. This is an important step toward integration of ecosystem models with remotely sensed information, global climate models, and atmospheric transport models, all of which are evaluated at comparable spatial and temporal scales. Seasonal patterns of NPP in South America are correlated with moisture availability in most vegetation types, but are strongly influenced by seasonal differences in cloudiness in the tropical evergreen forests. On an annual basis, moisture availability was the factor that was correlated most strongly with annual NPP in South America, but differences were again observed among vegetation types. These results allow for the investigation and analysis of climatic controls over NPP at continental scales, within and among vegetation types, and within years. Further model validation is needed. Nevertheless, the ability to investigate NPP—environment interactions with a high spatial and temporal resolution at continental scales should prove useful if not essential for rigorous analysis of the potential effects of global climate changes on terrestrial ecosystems

Nitrogen dynamics in arctic tundra soils of varying age : differential responses to fertilization and warming

Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Oecologia 173 (2013): 1575-1586, doi:10.1007/s00442-013-2733-5.In the northern foothills of the Brooks Range, Alaska, a series of glacial retreats has created a landscape that varies widely in time since deglaciation (= soil age), from ~10k years to more than 2M years. Productivity of the moist tundra that covers most of this landscape is generally N-limited, but varies widely, as do plant-species composition and key soil properties such as pH. These differences might be altered in the future because of the projected increase in N availability under a warmer climate. We hypothesized that future changes in productivity and vegetation composition across soil ages might be mediated through changes in N availability. To test this hypothesis, we compared readily available-N (water-soluble ammonium, nitrate, and amino acids), moderately-available N (soluble proteins), hydrolysable-N, and total-N pools across three tussock-tundra landscapes with soil ages ranging from 11.5k to 300k years. We also compared the effects of long-term fertilization and warming on these N pools for the two younger sites, in order to assess whether the impacts of warming and increased N availability differ by soil age. Readily available N was largest at the oldest site, and amino acids (AA) accounted for 80-89 % of this N. At the youngest site, however, inorganic N constituted the majority (80-97%) of total readily-available N. This variation reflected the large differences in plant functional-group composition and soil chemical properties. Long-term (8-16 years) fertilization increased soluble inorganic N by 20-100 fold at the intermediate-age site, but only by 2-3 fold at the youngest-soil site. Warming caused small and inconsistent changes in the soil C:N ratio and soluble AA, but only in soils beneath Eriophorum vaginatum, the dominant tussock-forming sedge. These differential responses suggest that the impacts of warmer climates on these tundra ecosystems are more complex than simply elevated N mineralization, and that the response of the N cycling might differ strongly depending on the ecosystem’s soil age, initial soil properties, and plant-community composition.Primary financial support came from NSF grant #DEB-0444592 to the MBL, and additional logistical support from NSF-OPP

Ecosystem responses to climate change at a Low Arctic and a High Arctic long-term research site

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ambio 46, Supple. 1 (2017): 160-173, doi:10.1007/s13280-016-0870-x.Long-term measurements of ecological effects of warming are often not statistically significant because of annual variability or signal noise. These are reduced in indicators that filter or reduce the noise around the signal and allow effects of climate warming to emerge. In this way, certain indicators act as medium pass filters integrating the signal over years-to-decades. In the Alaskan Arctic, the 25-year record of warming of air temperature revealed no significant trend, yet environmental and ecological changes prove that warming is affecting the ecosystem. The useful indicators are deep permafrost temperatures, vegetation and shrub biomass, satellite measures of canopy reflectance (NDVI), and chemical measures of soil weathering. In contrast, the 18-year record in the Greenland Arctic revealed an extremely high summer air-warming of 1.3°C/decade; the cover of some plant species increased while the cover of others decreased. Useful indicators of change are NDVI and the active layer thickness.The Toolik research was supported in part by NSF Grants DEB 0207150, DEB 1026843, ARC 1107701, and ARC 1504006

Herbivore absence can shift dry heath tundra from carbon source to sink during peak growing season

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Min, E., Wilcots, M. E., Naeem, S., Gough, L., McLaren, J. R., Rowe, R. J., Rastetter, E. B., Boelman, N. T., & Griffin, K. L. Herbivore absence can shift dry heath tundra from carbon source to sink during peak growing season. Environmental Research Letters, 16(2), (2021): 024027, https://doi.org/10.1088/1748-9326/abd3d0.In arctic tundra, large and small mammalian herbivores have substantial impacts on the vegetation community and consequently can affect the magnitude of carbon cycling. However, herbivores are often absent from modern carbon cycle models, partly because relatively few field studies focus on herbivore impacts on carbon cycling. Our objectives were to quantify the impact of 21 years of large herbivore and large and small herbivore exclusion on carbon cycling during peak growing season in a dry heath tundra community. When herbivores were excluded, we observed a significantly greater leaf area index as well as greater vascular plant abundance. While we did not observe significant differences in deciduous dwarf shrub abundance across treatments, evergreen dwarf shrub abundance was greater where large and small herbivores were excluded. Both foliose and fruticose lichen abundance were higher in the large herbivore, but not the small and large herbivore exclosures. Net ecosystem exchange (NEE) likewise indicated the highest carbon uptake in the exclosure treatments and lowest uptake in the control (CT), suggesting that herbivory decreased the capacity of dry heath tundra to take up carbon. Moreover, our calculated NEE for average light and temperature conditions for July 2017, when our measurements were taken, indicated that the tundra was a carbon source in CT, but was a carbon sink in both exclosure treatments, indicating removal of grazing pressure can change the carbon balance of dry heath tundra. Collectively, these findings suggest that herbivore absence can lead to changes in plant community structure of dry heath tundra that in turn can increase its capacity to take up carbon.The authors would like to thank Jess Steketee, Austin Roy, Matthew Suchocki, Ruby An, Cody Lane and the Arctic LTER (NSF Grant No. 1637459) for maintaining the long-term herbivore exclosure experiment. This work was supported by funding from the NSF (Grant Nos. OPP-1603677 to J R M, OPP-1603760 to L G, OPP-1603654 to R J R, OPP-1603560 to E R, OPP-1603777 to N B and K L G). We also acknowledge financial support for Megan Wilcots from the Department of Ecology, Evolution, and Environmental Biology at Columbia University

Nitrogen dynamics in a small arctic watershed: retention and downhill movement of 15N

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Monographs 80 (2010): 331-351, doi:10.1890/08-0773.1.We examined short- and long-term nitrogen (N) dynamics and availability along an arctic hillslope in Alaska, USA, using stable isotope of nitrogen (15N), as a tracer. Tracer levels of 15NH4+ were sprayed once onto the tundra at six sites in four tundra types; heath (crest), tussock with high and low water flux (mid- and foot-slope), and wet sedge (riparian). 15N in vegetation and soil was monitored to estimate retention and loss over a 3-yr period. Nearly all 15NH4+ was immediately retained in the surface moss-detritus-plant layer and > 57 % of the 15N added remained in this layer at the end of the second year. Organic soil was the second largest 15N sink. By the end of the third growing season, the moss-detritus-plant layer and organic soil combined retained ≥ 87 % of the 15N added except at the mid-slope site with high water flux, where recovery declined to 68 %. At all sites, non-extractable and non-labile-N pools were the principal sinks for added 15N in the organic soil. Hydrology played an important role in downslope movement of dissolved 15N. Crest and mid-slope with high water flux sites were most susceptible to 15N losses via leaching perhaps because of deep permeable mineral soil (crest) and high water flow (mid-slope with high water flux). Late spring melt-season also resulted in downslope dissolved-15N losses, perhaps because of an asynchrony between N release into melt water and soil immobilization capacity. We conclude that separation of the rooting zone from the strong sink for incoming N in the moss detritus-plant layer, rapid incorporation of new N into relatively recalcitrant soil-N pools within the rooting zone, and leaching loss from the upper hillslope would all contribute to the strong N limitation of this ecosystem. An extended snow-free season and deeper depth of thaw under warmer climate may significantly alter current N dynamics in this arctic ecosystem.Funding was provided by NSF grant #0444592. Additional support was provided by Toolik Field Station Long Term Ecological Research program, funded by National Science Foundation, Office of Polar Programs

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

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