Station Long-Term Ecological Research Research Articles

Machine learning reveals dynamic controls of soil nitrous oxide emissions from diverse long-term cropping systems.

Soil nitrous oxide (N2O) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003-2019) of measurements at the Kellogg Biological Station Long-Term Ecological Research (LTER)/Long-Term Agroecosystem Research (LTAR) site to better understand the controls of N2O emissions in four corn-soybean-winter wheat rotations employing conventional, no-till, reduced input, and biologically based/organic inputs. We used a random forest machine learning model to predict daily N2O fluxes, trained separately for each system with 70% of observations, using variables such as crop species, daily air temperature, cumulative 2-day precipitation, water-filled pore space, and soil nitrate and ammonium concentrations. The model explained 29%-42% of daily N2O flux variability in the test data, with greater predictability for the corn phase in each system. The long-term rotations showed different controlling factors and threshold conditions influencing N2O emissions. In the conventional system, the model identified ammonium (>15 kg N ha-1) and daily air temperature (>23°C) as the most influential variables; in the no-till system, climate variables such as precipitation and air temperature were important variables. In low-input and organic systems, where red clover (Trifolium repens L.; before corn) and cereal rye (Secale cereale L.; before soybean) cover crops were integrated, nitrate was the predominant predictor of N2O emissions, followed by precipitation and air temperature. In low-input and biologically based systems, red clover residues increased soil nitrogen availability to influence N2O emissions. Long-term data facilitated machine learning for predicting N2O emissions in response to differential controls and threshold responses to management, environmental, and biogeochemical drivers.

Gulf of Naples Advanced Model (GNAM): A Multiannual Comparison with Coastal HF Radar Data and Hydrological Measurements in a Coastal Tyrrhenian Basin

High-resolution modelling systems have increasingly become an essential requirement to investigate ocean dynamics over a wide range of spatial and temporal scales, and to integrate the punctual ocean observations. When applied in coastal areas, they also have the potential to provide a detailed representation of transport and exchange processes at the sub-basin scale. This paper presents a validation exercise between the surface fields generated by the regional ocean modeling system (ROMS), developed for the Tyrrhenian Sea and downscaled for the Gulf of Naples (GNAM Gulf of Naples advanced model), and a 4 year-long (2009–2012) record of high-frequency radar (HFR) data. The comparison between hourly and seasonal model results and HFR surface fields is focused on the Gulf of Naples (GoN), where an observational network of three HFR sites has been operational since 2004, and on a specific subdomain characterized by the presence of the Sarno river, a long-term ecological research station (LTER-MC) and one important canyon area. An evaluation on a transect delimiting inshore–offshore zones in the GoN is also presented. The GNAM model was also compared with in situ hydrological parameters of temperatures and salinities retrieved at the LTER-MC fixed monitoring station. According to the skill metrics, basic circulation features are accurately reproduced by the circulation model, despite some model drawbacks in terms of increment of energy content in the surface current field occurring during specific seasonal events. The results allow us to identify potential model errors and to suggest useful improvements, the outcome also confirms the unique capability of HF radar systems to provide fine-scale measurements for the validation of numerical models and to counterbalance the lack of high-resolution measurements in coastal areas.

Arbuscular mycorrhizal fungal community responses to drought and nitrogen fertilization in switchgrass stands

Anthropogenic global change is increasing the severity and frequency of abiotic stresses such as drought that are likely to affect soil communities. Arbuscular mycorrhizal fungi (AMF) play important roles in many soil processes, so it is important to understand how drought affects AMF biodiversity. This is especially relevant in agricultural systems where crops rely on AMF associations for water and nutrient uptake, and where management decisions such as crop selection and fertilizer application may influence how the AMF community responds to drought. In this study, we examined the effects of reduced precipitation and nitrogen fertilization on AMF richness, community composition, and root and soil colonization in monocultures of two cultivars of switchgrass (Panicum virgatum) grown for bioenergy feedstock. We conducted a two-year field experiment using rain-out shelters to manipulate precipitation in mature stands of switchgrass growing in a long-term nitrogen fertilization (0 or 56 kg N ha−1) experiment at the W.K. Kellogg Biological Station Long-Term Ecological Research site in Michigan, USA. We expected that AMF richness and colonization would decrease due to drought, as predicted by the stress exclusion hypothesis. Contrary to our expectations, we found that drought stress increased AMF species richness in fertilized plots by 15%; there was no effect of drought on AMF richness in unfertilized plots. Drought also significantly altered AMF community composition, primarily due to increases in Rhizophagus taxa abundance, and reduced AMF root colonization in switchgrass by 6%. We also found variation in AMF richness and colonization across switchgrass cultivars as well as sampling dates. The changes in AMF richness and composition that we observed in this study may have implications for perennial bioenergy feedstock selection and management as changes in AMF communities may feedback to affect host plants.

After the Blast

After the Blast

The broken window: An algorithm for quantifying and characterizing misleading trajectories in ecological processes

A core issue in temporal ecology is the concept of trajectory—that is, when can ecologists have reasonable assurance that they know where a system is going? In this paper, we describe a non-random resampling method to directly address the temporal aspects of scaling ecological observations by leveraging existing data. Findings from long-term research sites have been hugely influential in ecology because of their unprecedented longitudinal perspective, yet short-term studies more consistent with typical grant cycles and graduate programs are still the norm. We use long-term insights to create ‘broken windows,’ that is, reanalyze long-term studies from short-term observational perspectives to examine discontinuities in trends at differing temporal scales.The broken window algorithm connects our observations between the short-term and the long-term with an automated, systematic resampling approach: in short, we repeatedly ‘sample’ moving windows of data from existing long-term time series, and analyze these sampled data as if they represented the entire dataset. We then compile typical statistics used to describe the relationship in the sampled data, through repeated samplings, and then use these derived data to gain insights to the questions: 1) how often are the trends observed in short-term data misleading, and 2) can characteristics of these trends be used to predict our likelihood of being misled? We develop a systematic resampling approach, the ‘broken_window algorithm, and illustrate its utility with a case study of firefly observations produced at the Kellogg Biological Station Long-Term Ecological Research Site (KBS LTER). Through a variety of visualizations, summary statistics, and downstream analyses, we provide a standardized approach to evaluating the trajectory of a system, the amount of observation required to find a meaningful trajectory in similar systems, and a means of evaluating our confidence in our conclusions.

Host-parasitoid associations in marine planktonic time series: Can metabarcoding help reveal them?

In this study, we created a dataset of a continuous three-year 18S metabarcoding survey to identify eukaryotic parasitoids, and potential connections to hosts at the Long-Term Ecological Research station Helgoland Roads. The importance of parasites and parasitoids for food web dynamics has previously been recognized mostly in terrestrial and freshwater systems, while marine planktonic parasitoids have been understudied in comparison to those. Therefore, the occurrence and role of parasites and parasitoids remains mostly unconsidered in the marine environment. We observed high abundances and diversity of parasitoid operational taxonomic units in our dataset all year round. While some parasitoid groups were present throughout the year and merely fluctuated in abundances, we also detected a succession of parasitoid groups with peaks of individual species only during certain seasons. Using co-occurrence and patterns of seasonal occurrence, we were able to identify known host-parasitoid dynamics, however identification of new potential host-parasitoid interactions was not possible due to their high dynamics and variability in the dataset.

Evaluating the applicability of soil moisture-based metrics for gauging the resiliency of rainfed agricultural systems in the midwestern United States

Measuring and improving resiliency is the key goal of climate risk management in rainfed agriculture. Currently, available metrics are generally used to qualitatively measure the resilience of agricultural systems at broader scales. Moreover, these metrics showed non-linear responses to climate variability, thus often fail to capture the temporal dynamics of resilience at field scales. Our objective for this study is to combine a few soil moisture-based metrics to gauge the resiliency of three promising rainfed agricultural treatments, namely the no-till, organic, and reduced input treatments against the conventional treatment. These treatments have been established at the Kellogg Biological Station Long-Term Ecological Research (KBS-LTER) experiment in a randomized complete block design with six blocks per treatment. All four of these treatments consisted of maize (Zea mays)-soybean (Glycine max)-winter wheat (Triticum aestivum) in rotation. Long-term (1993–2018) soil moisture data from this experiment was collected to compute the soil moisture metrics while the total crop biomass, crop yield, and soil organic carbon data were statistically analyzed to evaluate the robustness of the metrics to gauge the resiliency of these systems. Results have shown that, among the soil moisture metrics, the mean relative difference, the Spearman’s rank correlation coefficient, and soil water deficit index were suitable, while the index of temporal stability was not suitable to gauge the resiliency of different rainfed agricultural systems. The no-till treatment was identified as the most resilient treatment in terms of soil moisture retention, effectiveness for drought mitigation, and crop yields. Meanwhile, the reduced input treatment was the least resilient in terms of soil water conservation and drought recovery. The results of this study can be extended to other Midwest regions of the United States and similar climatological areas around the world.

Microbial processing of sedimentary organic matter at a shallow LTER site in the northern Adriatic Sea: an 8-year case study

Benthic prokaryotes are the key-players in C-cycling at the sediment-seawater interface, one of the largest biologically active interfaces on Earth. Here, microbial-mediated processes, such as the degradation of organic matter and the incorporation of mobilized C into microbial biomass, depend on several factors such as environmental temperature and substrate availability, especially in shallow sediments at mid-high latitudes where seasonal fluctuations of these variables occur. In the present study, four degradative activities (β-glucosidase, lipase, chitinase and aminopeptidase), Heterotrophic C Production (HCP), Total Organic C (TOC), Total Nitrogen (TN) and Biopolymeric C (BPC) were investigated seasonally from April 2010 to April 2018 in the surface sediments of a shallow Long-Term Ecological Research (LTER) station of the northern Adriatic Sea. Significant temperature-dependences were described by Arrhenius-type equations for HCP and each of the degradative activities tested with the exception of aminopeptidase. The relatively low apparent Activation Energies suggested that these microbial-mediated processes were enhanced by the availability of palatable substrates over the study period. Nevertheless, a clear and tight dependence from such substrates was detected only for aminopeptidase, the most pronounced degradative activity observed. TN was identified by the stepwise multiple regression analysis as the environmental variable that mainly drove this exoenzymatic activity. Enhanced aminopeptidase rates mirrored peaks of TN that seemed, in turn, linked to the seasonal proliferation of benthic microalgae. By supplying prokaryotes with promptly available substrates, these autotrophs, represented mainly by diatoms, seemed to play an important role in the C-cycling regulation at the studied LTER station.

Phytoplankton temporal dynamics in the coastal waters of the north-eastern Adriatic Sea (Mediterranean Sea) from 2010 to 2017

Phytoplankton community structure was analysed from 2010 to 2017 at C1-LTER, the coastal Long-Term Ecological Research station located in the Gulf of Trieste, which is the northernmost part of the Mediterranean Sea. Phytoplankton abundance and relevant oceanographic parameters were measured monthly in order to describe the seasonal cycle and interannual variability of the main phytoplankton taxa (diatoms, dinoflagellates, coccolithophores and flagellates) and to analyse their relationship with environmental conditions. Overall, phytoplankton abundances showed a marked seasonal cycle characterised by a bloom in spring, with the peak in May. During the summer, phytoplankton abundances gradually decreased until September, then slightly increased again in October and reached their minima in winter. In general, the phytoplankton community was dominated by flagellates (generally <10 µm) and diatoms co-occurring in the spring bloom. In this period, diatoms were also represented by nano-sized species, gradually replaced by larger species in summer and autumn. Phytoplankton assemblages differed significantly between seasons (Pseudo-F= 9.59;p< 0.01) and temperature and salinity were the best predictor variables explaining the distribution of the multivariate data cloud. At the interannual scale, a strong decrease of the late-winter bloom was observed in recent years with the spring bloom being the main phytoplankton increase of the year.

In Situ Quantification of Biological N2 Production Using Naturally Occurring 15N15N.

We describe an approach for determining biological N2 production in soils based on the proportions of naturally occurring 15N15N in N2. Laboratory incubation experiments reveal that biological N2 production, whether by denitrification or anaerobic ammonia oxidation, yields proportions of 15N15N in N2 that are within 1‰ of that predicted for a random distribution of 15N and 14N atoms. This relatively invariant isotopic signature contrasts with that of the atmosphere, which has 15N15N proportions in excess of the random distribution by 19.1 ± 0.1‰. Depth profiles of gases in agricultural soils from the Kellogg Biological Station Long-Term Ecological Research site show biological N2 accumulation that accounts for up to 1.6% of the soil N2. One-dimensional reaction-diffusion modeling of these soil profiles suggests that subsurface N2 pulses leading to surface emission rates as low as 0.3 mmol N2 m–2 d–1 can be detected with current analytical precision, decoupled from N2O production.

The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images

ABSTRACTThe impact of structural parameters of agricultural crops on the retrieval of chlorophyll content presents a real challenge for the remote-sensing community. Canopy reflectance can differ between crops of different canopy structure even when they have the same canopy chlorophyll content. Thus, structural properties should be incorporated in chlorophyll mapping to reduce modelling errors. The empirical relationships between vegetation indices and chlorophyll content are well established and commonly used in precision agriculture. Recent advances in using unmanned aerial vehicle (UAV/drone) technology offer successful retrieval of crop structural and biochemical parameters. However, transfer of empirical algorithms derived from satellite to UAV-based analyses introduces new challenges mainly due to fine spatial resolution and details such as crop rows and between- and within-canopy gaps that are more pronounced in UAV images. There are two components of the analysis in this study. The first part is related to heterogeneity of leaf area index (LAI) and chlorophyll content of corn under four agricultural treatments (conventional ploughed, conventional with no tilling, biological with reduced chemical inputs, and certified organic) at the Kellogg Biological Station Long-Term Ecological Research (KBS LTER) site in Michigan, USA. The second part examines the necessity and importance of LAI in chlorophyll mapping using UAV images collected over the heterogeneous KBS LTER parcels at peak growing season. The UAV-derived normalized difference red edge index (NDRE) is found to be highly correlated with canopy chlorophyll, calculated as a product of leaf chlorophyll content and LAI. The coefficient of determination changes from R2 = 0.177 to R2 = 0.774 when LAI is added to the empirical model. NDRE is also found to be highly correlated with LAI (R2 = 0.620). The findings suggest that the conventional corn treatment with no-tilled soil exhibits the highest crop vigour during the peak growing season. The herbicide management applied earlier in the season may have a strong effect on weeds, reducing the crop–weeds competition for nutrients.

Weed Diversity Affects Soybean and Maize Yield in a Long Term Experiment in Michigan, USA.

Managing production environments in ways that promote weed community diversity may enhance both crop production and the development of a more sustainable agriculture. This study analyzed data of productivity of maize (corn) and soybean in plots in the Main Cropping System Experiment (MCSE) at the W. K. Kellogg Biological Station Long-Term Ecological Research (KBS-LTER) in Michigan, USA, from 1996 to 2011. We used models derived from population ecology to explore how weed diversity, temperature, and precipitation interact with crop yields. Using three types of models that considered internal and external (climate and weeds) factors, with additive or non-linear variants, we found that changes in weed diversity were associated with changes in rates of crop yield increase over time for both maize and soybeans. The intrinsic capacity for soybean yield increase in response to the environment was greater under more diverse weed communities. Soybean production risks were greatest in the least weed diverse systems, in which each weed species lost was associated with progressively greater crop yield losses. Managing for weed community diversity, while suppressing dominant, highly competitive weeds, may be a helpful strategy for supporting long term increases in soybean productivity. In maize, there was a negative and non-additive response of yields to the interaction between weed diversity and minimum air temperatures. When cold temperatures constrained potential maize productivity through limited resources, negative interactions with weed diversity became more pronounced. We suggest that: (1) maize was less competitive in cold years allowing higher weed diversity and the dominance of some weed species; or (2) that cold years resulted in increased weed richness and prevalence of competitive weeds, thus reducing crop yields. Therefore, we propose to control dominant weed species especially in the years of low yield and extreme minimum temperatures to improve maize yields. Results of our study indicate that through the proactive management of weed diversity, it may be possible to promote both high productivity of crops and environmental sustainability.

Six Pseudoalteromonas Strains Isolated from Surface Waters of Kabeltonne, Offshore Helgoland, North Sea.

Draft genomes are presented for 6 Pseudoalteromonas sp. strains isolated from surface waters at Kabeltonne, Helgoland, a long-term ecological research station in the North Sea. These strains contribute knowledge of the genomic underpinnings of a developing model system to study phage-host dynamics of a particle-associated ocean copiotroph.

Pteropod sedimentation patterns in different water depths observed with moored sediment traps over a 4-year period at the LTER station HAUSGARTEN in eastern Fram Strait

Pteropods are important organisms in high-latitude ecosystems, and they are expected to severely suffer from climate change in the near future. In this study, sedimentation patterns of two pteropod species, the polar Limacina helicina and the subarctic boreal L. retroversa, are presented. Time series data received by moored sediment traps at the Long-Term Ecological Research (LTER) Observatory HAUSGARTEN in eastern Fram Strait were analyzed during the years 2008 to 2012. Results were derived from four different deployment depths (~200, 1,250, 2,400, and 2,550 m) at two different sites (79°N 04°20′E; 79°43′N 04°30′E). A species-specific sedimentation pattern was present at all depths and at both sites showing maximal flux rates during September/October for L. helicina and in November/December for L. retroversa. The polar L. helicina was outnumbered by L. retroversa (55–99 %) at both positions and at all depths supporting the recently observed trend toward the dominance of the subarctic boreal species. The largest decrease in pteropod abundance occurred within the mesopelagic zone (~200–1,250 m), indicating loss via microbial degradation and grazing. Pteropod carbonate (aragonite) amounted up to ~75 % of the total carbonate flux at 200 m and 2–13 % of the aragonite found in the shallow traps arrived at the deep sediment traps (~160 m above the seafloor), revealing the significance of pteropods in carbonate export at Fram Strait. Our results emphasize the relevance and the need for continuation of long-term studies to detect and trace changes in pteropod abundances and community composition and thus in the vertical transport of aragonite.

Complete genome sequence of the lignin-degrading bacterium Klebsiella sp. strain BRL6-2.

In an effort to discover anaerobic bacteria capable of lignin degradation, we isolated Klebsiella sp. strain BRL6-2 on minimal media with alkali lignin as the sole carbon source. This organism was isolated anaerobically from tropical forest soils collected from the Bisley watershed at the Ridge site in the El Yunque National Forest in Puerto Rico, USA, part of the Luquillo Long-Term Ecological Research Station. At this site, the soils experience strong fluctuations in redox potential and are characterized by cycles of iron oxidation and reduction. Genome sequencing was targeted because of its ability to grow on lignin anaerobically and lignocellulolytic activity via in vitro enzyme assays. The genome of Klebsiella sp. strain BRL6-2 is 5.80 Mbp with no detected plasmids, and includes a relatively small arsenal of genes encoding lignocellulolytic carbohydrate active enzymes. The genome revealed four putative peroxidases including glutathione and DyP-type peroxidases, and a complete protocatechuate pathway encoded in a single gene cluster. Physiological studies revealed Klebsiella sp. strain BRL6-2 to be relatively stress tolerant to high ionic strength conditions. It grows in increasing concentrations of ionic liquid (1-ethyl-3-methyl-imidazolium acetate) up to 73.44 mM and NaCl up to 1.5 M.

Farming for Ecosystem Services: An Ecological Approach to Production Agriculture.

A balanced assessment of ecosystem services provided by agriculture requires a systems-level socioecological understanding of related management practices at local to landscape scales. The results from 25 years of observation and experimentation at the Kellogg Biological Station long-term ecological research site reveal services that could be provided by intensive row-crop ecosystems. In addition to high yields, farms could be readily managed to contribute clean water, biocontrol and other biodiversity benefits, climate stabilization, and long-term soil fertility, thereby helping meet society's need for agriculture that is economically and environmentally sustainable. Midwest farmers—especially those with large farms—appear willing to adopt practices that deliver these services in exchange for payments scaled to management complexity and farmstead benefit. Surveyed citizens appear willing to pay farmers for the delivery of specific services, such as cleaner lakes. A new farming for services paradigm in US agriculture seems feasible and could be environmentally significant.

Diversity and germination patterns of diatom resting stages at a coastal Mediterranean site

Many planktonic diatoms produce resting stages that survive in the sediment and allow species persistence over periods in which vegetative growth does not occur. With the aim of depicting the annual variability of diatom benthic stage production and understanding the rela- tionships between planktonic and benthic dynamics, we quantified diatoms in surface waters and their viable resting stages in surface sediments at the long-term ecological research station MareChiara (LTER-MC) in the Gulf of Naples (Mediterranean Sea) over 15 mo. A considerable fraction of the diatom species recorded in the water column was also obtained from germination in surface sediments, showing that the formation of resting stages is a widespread life strategy in coastal areas. The most abundant taxa recorded in the plankton were several species of the genera Chaetoceros, Skeletonema, Thalassiosira, Cyclotella, Pseudo-nitzschia and Bacteriastrum. Species of the latter 2 genera were never retrieved from the sediment. Peaks of viable resting stages were generally recorded in the same period as blooms of the individual species in the plankton. Repeated germination experiments with the same sediments demonstrated that some species are more resistant than others to prolonged darkness, while different daylength conditions did not produce a clear effect on germination patterns. This first time-series analysis of a coastal seed bank demonstrates that the assemblage of viable diatoms in the sediment varies over the year, reflecting the diatom dyna mics in the water column, but also species-specific characteristics in survival capability and likely the disturbance due to biotic and abiotic factors.

Complete genome sequence of "Enterobacter lignolyticus" SCF1.

In an effort to discover anaerobic bacteria capable of lignin degradation, we isolated “Enterobacter lignolyticus” SCF1 on minimal media with alkali lignin as the sole source of carbon. This organism was isolated anaerobically from tropical forest soils collected from the Short Cloud Forest site in the El Yunque National Forest in Puerto Rico, USA, part of the Luquillo Long-Term Ecological Research Station. At this site, the soils experience strong fluctuations in redox potential and are net methane producers. Because of its ability to grow on lignin anaerobically, we sequenced the genome. The genome of “E. lignolyticus” SCF1 is 4.81 Mbp with no detected plasmids, and includes a relatively small arsenal of lignocellulolytic carbohydrate active enzymes. Lignin degradation was observed in culture, and the genome revealed two putative laccases, a putative peroxidase, and a complete 4-hydroxyphenylacetate degradation pathway encoded in a single gene cluster.

Bacterial gene abundances as indicators of greenhouse gas emission in soils

Nitrogen fixing and denitrifying bacteria, respectively, control bulk inputs and outputs of nitrogen in soils, thereby mediating nitrogen-based greenhouse gas emissions in an ecosystem. Molecular techniques were used to evaluate the relative abundances of nitrogen fixing, denitrifying and two numerically dominant ribotypes (based on the > or =97% sequence similarity at the 16S rRNA gene) of bacteria in plots representing 10 agricultural and other land-use practices at the Kellogg biological station long-term ecological research site. Quantification of nitrogen-related functional genes (nitrite reductase, nirS; nitrous oxide reductase, nosZ; and nitrogenase, nifH) as well as two dominant 16S ribotypes (belonging to the phyla Acidobacteria, Thermomicrobia) allowed us to evaluate the hypothesis that microbial community differences are linked to greenhouse gas emissions under different land management practices. Our results suggest that the successional stages of the ecosystem are strongly linked to bacterial functional group abundance, and that the legacy of agricultural practices can be sustained over decades. We also link greenhouse gas emissions with specific compositional responses in the soil bacterial community and assess the use of denitrifying gene abundances as proxies for determining nitrous oxide emissions from soils.

Seven years of continuously planted Bt corn did not affect mineralizable and total soil C and total N in surface soil

Genetically engineered corn (Zea mays L.) containing a gene from the soil bacterium Bt (Bacillus thuringiensis) constitutes a large proportion of all corn planted in the United States. In a number of studies, Bt plant residues have been reported to have higher lignin content and to decompose slower than those of non-Bt plants, possibly due to the presence of the Cry endotoxin. We hypothesize that after multiple years of continuous cultivation of Bt corn, the combined results of alleged differences in Bt corn residues and of Cry endotoxin presence will be reflected in soil characteristics, specifically in an increase in soil total carbon (C) and nitrogen (N) levels. We collected soil samples at 0–7.5 cm depth in 2006 from continuous Bt and non-Bt corn treatments in an randomized complete block design experiment with four replications. The experiment was established in 1999 at the Kellogg Biological Station long-term ecological research (LTER) site in southwest Michigan. We found that, after 7 years of Bt cropping, neither total soil C and N nor soil C mineralized during a 35-day incubation were significantly different between Bt and non-Bt corn treatments (P > 0.05). Total soil C was equal to 7.3 g kg−1 and 7.4 g kg−1, in Bt and non-Bt corn, respectively, with a standard error of the means (SEM) = 0.2, and total N was 0.67 g kg−1 (SEM 0.02) in both treatments. Post-hoc power analysis indicated that, given the number of samples collected in this study and the observed level of variability, the minimal differences between the Bt and non-Bt treatments that could be detected as statistically significant at α = 0.05 with a power of 0.80 were equal to 1.0 g kg−1, 0.14 g kg−1 and 0.125 g kg−1 for total C, total N, and soil C mineralized during a 35-day incubation, respectively. The results indicate that continuous Bt corn production during a 7-year period did not lead to sizeable changes in total soil C and N.