Aims: This review was developed to introduce the essential components and variants of structural equation modeling (SEM), synthesize the common issues in SEM applications, and share our views on SEM's future in ecological research. Methods: We searched the Web of Science on SEM applications in ecological studies from 1999 through 2016 and summarized the potential of SEMs, with a special focus on unexplored uses in ecology. We also analyzed and discussed the common issues with SEM applications in previous publications and presented our view for its future applications.Results: We searched and found 146 relevant publications on SEM applications in ecological studies. We found that five SEM variants had not commenly been applied in ecology, including the latent growth curve model, Bayesian SEM, partial least square SEM, hierarchical SEM, and variable/model selection. We identified ten common issues in SEM applications including strength of causal assumption, specification of feedback loops, selection of models and variables, identification of models, methods of estimation, explanation of latent variables, selection of fit indices, report of results, estimation of sample size, and the fit of model. Conclusions:In previous ecological studies, measurements of latent variables, explanations of model parameters, and reports of key statistics were commonly overlooked, while several advanced uses of SEM had been ignored overall. With the increasing availability of data, the use of SEM holds immense potential for ecologists in the future.
[1] We developed a water-centric monthly scale simulation model (WaSSI-C) by integrating empirical water and carbon flux measurements from the FLUXNET network and an existing water supply and demand accounting model (WaSSI). The WaSSI-C model was evaluated with basin-scale evapotranspiration (ET), gross ecosystem productivity (GEP), and net ecosystem exchange (NEE) estimates by multiple independent methods across 2103 eight-digit Hydrologic Unit Code watersheds in the conterminous United States from 2001 to 2006. Our results indicate that WaSSI-C captured the spatial and temporal variability and the effects of large droughts on key ecosystem fluxes. Our modeled mean (±standard deviation in space) ET (556 ± 228 mm yr −1 ) compared well to Moderate Resolution Imaging Spectroradiometer (MODIS) based (527 ± 251 mm yr −1 ) and watershed water balance based ET (571 ± 242 mm yr −1 ). Our mean annual GEP estimates (1362 ± 688 g C m −2 yr −1 ) compared well (R 2 = 0.83) to estimates (1194 ± 649 g C m −2 yr −1 ) by eddy flux-based EC-MOD model, but both methods led significantly higher (25-30%) values than the standard MODIS product (904 ± 467 g C m −2 yr −1 ). Among the 18 water resource regions, the southeast ranked the highest in terms of its water yield and carbon sequestration capacity. When all ecosystems were considered, the mean NEE (−353 ± 298 g C m −2 yr −1 ) predicted by this study was 60% higher than EC-MOD's estimate (−220 ± 225 g C m −2 yr −1) in absolute magnitude, suggesting overall high uncertainty in quantifying NEE at a large scale. Our water-centric model offers a new tool for examining the trade-offs between regional water and carbon resources under a changing environment.
To understand the carbon and energy exchange between the lake surface and the atmosphere, direct measurements of latent, sensible heat, and CO 2 fluxes were taken using the eddy covariance (EC) technique in Western Lake Erie during October 2011 to September 2013. We found that the latent heat flux (LE) had a marked one-peak seasonal change in both years that differed from the diurnal course and lacked a sinusoidal dynamic common in terrestrial ecosystems. Daily mean LE was 4.8 ± 0.1 and 4.3 ± 0.2 MJ m À2 d À1 in Year 1 and Year 2, respectively. The sensible heat flux (H) remained much lower than the LE, with a daily mean of 0.9 ± 0.1 and 1.1 ± 0.1 MJ m À2 d À1 in Year 1 and Year 2, respectively. As a result, the Bowen ratio was <1 during most of the 2 year period, with the lowest summer value at 0.14. The vapor pressure deficit explained 35% of the variation in half hourly LE, while the temperature difference between the water surface and air explained 65% of the variation in half hourly H. Western Lake Erie acted as a small carbon sink holding À19.0 ± 5.4 and À40.2 ± 13.3 g C m À2 in the first and second summers (May-September) but as an annual source of 77.7 ± 18.6 and 49.5 ± 17.9 g C m À2 yr À1 in Year 1 and Year 2, respectively. The CO 2 flux (F CO2 ). Similar to LE, F CO2 had noticeable diurnal changes during the months that had high chlorophyll a months but not during other months. A significantly negative correlation (P < 0.05) was found between F CO2 and chlorophyll a on monthly fluxes. Three gap-filling methods, including marginal distribution sampling, mean diurnal variation, and monthly mean, were quantitatively assessed, yielding an uncertainty of 4%, 6%, and 10% in LE, H, and F CO2 , respectively.
FLUXNET, the global network of eddy covariance flux towers, provides the largest synthesized data set of CO2, H2O, and energy fluxes. To achieve the ultimate goal of providing flux information “everywhere and all of the time,” studies have attempted to address the representativeness issue, i.e., whether measurements taken in a set of given locations and measurement periods can be extrapolated to a space‐ and time‐explicit extent (e.g., terrestrial globe, 1982–2013 climatological baseline). This study focuses on the temporal representativeness of FLUXNET and tests whether site‐specific measurement periods are sufficient to capture the natural variability of climatological and biological conditions. FLUXNET is unevenly representative across sites in terms of the measurement lengths and potentials of extrapolation in time. Similarity of driver conditions among years generally enables the extrapolation of flux information beyond measurement periods. Yet such extrapolation potentials are further constrained by site‐specific variability of driver conditions. Several driver variables such as air temperature, diurnal temperature range, potential evapotranspiration, and normalized difference vegetation index had detectable trends and/or breakpoints within the baseline period, and flux measurements generally covered similar and biased conditions in those drivers. About 38% and 60% of FLUXNET sites adequately sampled the mean conditions and interannual variability of all driver conditions, respectively. For long‐record sites (≥15 years) the percentages increased to 59% and 69%, respectively. However, the justification of temporal representativeness should not rely solely on the lengths of measurements. Whenever possible, site‐specific consideration (e.g., trend, breakpoint, and interannual variability in drivers) should be taken into account.
Net ecosystem carbon dioxide (F CO2 ) and methane (F CH4 ) exchanges were measured by using the eddy covariance method to quantify the atmospheric carbon budget at a Typha-and Nymphaea-dominated freshwater marsh (March 2011 to March 2013) and a soybean cropland (May 2011 to May 2012) in northwestern Ohio, USA. Two year average annual F CH4 (49.7 g C-CH 4 m À2 yr À1) from the marsh was high and compatible with its net annual CO 2 uptake (F CO2 : À21.0 g C-CO 2 m À2 yr À1). In contrast, F CH4 was small (2.3 g C-CH 4 m À2 yr À1) and accounted for a minor portion of the atmospheric carbon budget (F CO2 : À151.8 g C-CO 2 m À2 yr À1) at the cropland. At the seasonal scale, soil temperature associated with methane (CH 4 ) production provided the dominant regulator of F CH4 at the marsh (R 2 = 0.86). At the diurnal scale, plant-modulated gas flow was the major pathway for CH 4 outgassing in the growing season at the marsh. Diffusion and ebullition became the major pathways in the nongrowing season and were regulated by friction velocity. Our findings highlight the importance of freshwater marshes for their efficiency in turning over and releasing newly fixed carbon as CH 4 . Despite marshes accounting for only~4% of area in the agriculture-dominated landscape, their high F CH4 should be carefully addressed in the regional carbon budget.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.