Advertisement

Hydrobiologia

, Volume 827, Issue 1, pp 211–224 | Cite as

Breakdown rates and associated nutrient cycling vary between novel crop-derived and natural riparian detritus in aquatic agroecosystems

  • Jason M. Taylor
  • Richard E. LizotteJr.
  • Sam TestaIII
Primary Research Paper

Abstract

Freshwater ecosystem function within agricultural landscapes may be altered by differences in processing of organic matter (OM) detritus entering freshwater habitats. We compared litter breakdown rates between crop residues; maize, cotton and soybean, and native riparian species: willow oak, American sycamore and cottonwood from inundated remnant river meander channels located within the Lower Mississippi River Basin (LMRB). Litter breakdown varied among the six species with the highest and lowest breakdown rates represented by crop (\(\bar{X}\) k day−1 = 0.007–0.011) and riparian species (\(\bar{X}\) k day−1 = 0.003–0.005), respectively. OM nutrient concentration varied widely across the six species. OM C:N ratios declined with time for all species except cotton. Temporal patterns in C:P ratios varied among crop residues but initially increased before declining for all three riparian species. Riparian OM breakdown rates were more negatively related to increasing C:N ratios of OM at the end of the study compared to crop species. Historic shifts in landscape-scale OM sources from diverse bottomland tree assemblages to crop residues has likely altered LMRB bayou and oxbow ecosystems by shifting both the timing and lability of OM pulses and decreasing long-term storage of OM, an important habitat and food resource for aquatic communities.

Keywords

Oxbow lake Bayou Agriculture Agroecosystem Breakdown Crop residue Riparian 

Introduction

Fluvial shallow lakes embedded within networks of sluggish, intermittently flowing freshwater systems (e.g., bayous) are dynamic habitat components of low-gradient large river floodplain ecosystems globally (Tundisi & Tundisi, 2012). These remnants of ancient river meanders are a common feature in the Lower Mississippi River Basin (LMRB). Organic matter (OM) in these systems is an integral component of local food webs and mechanisms controlling its breakdown and the cycling of associated nutrients can be an important factor regulating nutrient export to downstream marine ecosystems (Tockner et al., 1999; Langhans & Tockner, 2006; Rueda-Delgado et al., 2006). Oxbows and bayous in the LMRB are part of a highly modified agroecosystem due to geologic forces associated with large river systems depositing alluvial soils and, in the process, forming relatively flat, fertile lands that have become intensively used for agriculture (Locke et al., 2010). As a result, the Mississippi Alluvial Plain (MAP) of the LMRB (Fig. 1A) is intensively farmed in row-crop with soybeans, rice, maize, and cotton. The region accounts for approximately 17–20% of total cotton (Gossypium hirsutum L.) and 69–78% of total rice (Oryza sativa L.) production nationally, as well as significant maize (Zea mays L.) and soybean (Glycine max (L.) Merr.) yields (United States Department of Agriculture, National Agricultural Statistics Service, 2015). This is a common phenomenon within large river floodplains of the United States and around the world, and conversion of forested floodplains to intensive agriculture continues in order to meet growing global food, fiber and biofuel demands (Gennet et al., 2013; Hanberry et al., 2015; Yasarer et al., 2016). Organic matter processing within these dynamic freshwater habitats is potentially influenced by agriculture through indirect enrichment effects and direct effects of shifting OM sources (Taylor et al., 2017). Understanding factors that influence particulate organic matter processing within these ecosystems is paramount to understanding the evolving complexities of managing lands for high intensity food, fiber and biofuel production, while at the same time reducing ecological impacts associated with large scale agriculture.
Fig. 1

(A) Field sample sites in the Lower Mississippi River Basin (LMRB) in north-western Mississippi, USA. (B) Bayou freshwater habitat with intermittent flow within LMRB. (C) Litter bag deployment set-up including willow oak, soybean, American sycamore and maize, from top to bottom

Allochthonous organic matter decomposition is an integral component of heterotrophic nutrient cycling and food web interactions in freshwater ecosystems (Webster & Benfield, 1986; Tank et al., 2010). Carbon (C), nitrogen (N) and phosphorus (P) mineralization during the breakdown process fuels microbial production (Gulis & Suberkropp, 2003; Tank et al., 2010), transfers energy to higher trophic levels in freshwater food webs (Wallace et al., 1999; Johnson & Wallace, 2005; Scharnweber et al., 2014), and can also influence other microbial-mediated processes such as N retention and denitrification by providing labile C and N to denitrifying microbes (Stelzer et al., 2014). Despite considerable attention to measuring decomposition processes in streams, lakes, and wetlands, few studies have specifically investigated litter breakdown in oxbow lake or bayou ecosystems (Taylor et al., 2017). Litter breakdown in oxbow lakes and bayous is likely influenced by interactions between microbes, nutrient subsidies, and hydrology. For example, Taylor et al. (2017) observed that organic matter breakdown within inflow habitats of bayous was lower than more stable lentic habitats due to unstable hydrologic conditions experienced at inflow sites. High productivity associated with nutrient enrichment can also increase microbial processing of litter (Gulis et al., 2006; Paul et al., 2006; Kominoski et al., 2015) and experimental evidence suggests it plays a significant role in breakdown rates within LMRB bayous (Taylor et al., 2017).

Changes in the species composition and associated quality of organic matter inputs are also likely associated with conversion of floodplains to intensive mechanized row-crop agriculture. Previous studies have demonstrated significant transport of maize crop residue into adjacent freshwater habitats within agricultural landscapes (Rosi-Marshall et al., 2007; Jensen et al., 2010) and changes in relative contribution of crop residue vs native riparian species organic matter sources, coupled with relatively higher breakdown rates for maize, have major implications for in-stream organic matter processing within agroecosystems (Griffiths et al., 2009, 2012; Taylor et al., 2017). While there is a growing body of literature related to maize breakdown from diverse settings, additional information is needed for breakdown rates for other high commodity crops including soybeans and cotton. Crop litter has potentially higher nutrient concentrations than native riparian species due to direct application of fertilizers or N fixing capabilities. Additionally, crop litter generally has lower lignin concentration than riparian tree species (Ostrofsky, 1997; Ververis et al., 2004; Poerschmann et al., 2005; Peltier et al., 2009). Given that plant litter nutrient concentrations and lignin concentration play an important role in the initial colonization by microbes and overall breakdown rates (Gessner & Chauvet, 1994; Ostrofsky, 1997; Royer & Minshall, 2001), differences in lability and nutrient concentration may contribute to faster litter breakdown rates in crop versus riparian litter. Understanding how shifts in timing, magnitude and incorporation of crop residues into aquatic food webs is integral to understanding shifts in overall structure and function of agriculturally influenced freshwater ecosystems.

The current study investigates organic matter breakdown rates for three crop residue species including maize, soybean and cotton, and three riparian species including willow oak (Quercus phellos L.), American sycamore (Platanus occidentalis L.), and cottonwood (Populus deltoides Bartr. Ex. Marsh.) within three agriculturally influenced bayous located within the LMRB. Our objective was to evaluate differences in organic matter breakdown rates and associated changes in organic matter nutrient concentration between three common crop residue and three bottomland hardwood species. We hypothesized that: (1) riparian species would have lower organic matter breakdown rates than crop residue species and (2) breakdown rates would vary with organic matter nutrient concentration.

Methods

Field study area

The study was conducted in three low-gradient bayous draining intensive agricultural watersheds distributed across the northwest Mississippi portion of the LMRB (Fig. 1A). Bayous are a common freshwater habitat feature within the LMRB and can be characterized as seasonally flowing systems with extended lake-like conditions during low rainfall months (Fig. 1B). The study watersheds range in size from approximately 17–25 km2 with > 80% of the land-use in row-crop production during the study period. Primary row crops in the study bayou watersheds are soybean, cotton, maize, and rice. Because of this, these systems receive significant amounts of agricultural run-off including high sediment, nutrient, and carbon loads during the winter/spring wet season. Dominant tree species found within and along bayou banks include several Quercus spp. including willow oak, bald cypress (Taxodium distichum (L.) L.C. Rich.), sweetgum (Liquidambar styraciflua L.), American sycamore, and cottonwood. We chose willow oak, cottonwood, and sycamore as sources of natural organic matter because they are common species found in lowland floodplain habitats, and willow oak or similar oak species and cottonwood are commonly used in floodplain reforestation projects. Additionally, willow oak has been used in other organic matter breakdown studies within the region (Fuell et al., 2013; Taylor et al., 2017). We did not investigate bald cypress breakdown rates due to difficulties with maintaining brittle small needles within mesh bags. Multiple studies have investigated crop residue breakdown in aquatic systems using maize (Griffiths et al., 2009; Swan et al., 2009; Taylor et al., 2017). Our goal here, was to expand the current literature on crop residue breakdown rates by also including soybean and cotton in the current study. We did not use rice straw as a crop residue OM source because farmers typically burn rice fields after harvest within the LMRB (McCarty et al., 2009). Within each bayou, we used previously established lentic sites for litter bag deployment and water-quality monitoring.

Water physical and chemical variables

We sampled water biweekly from field sites for a total of 12 sampling events during the current study and used standard methods (APHA, 2005) for all analyses. Water samples were characterized for dissolved and total nutrients, suspended sediments, as well as total suspended solids (TSS), alkalinity, hardness, and turbidity. We determined surface water concentrations for dissolved NH4–N, NO3–N, and PO4–P concentrations after filtration (0.45 µm) using the phenolate, Cd reduction, and molybdate methods, respectively. Total Kjeldahl N and P were also analyzed with Cd reduction and molybdate methods, following a micro-Kjeldahl block digestion. All nutrient samples were run on a Lachat QuickChem 8500 Series auto analyzer (Lachat Instruments, Loveland, CO). We also measured dissolved organic carbon (DOC) on filtered samples (0.45 µm) using an Apollo 9000 Combustion TOC analyzer (Teledyne Tekmar, Mason, OH).

Litter breakdown

We measured litter breakdown at lentic sites within three bayous over 183 days between 18 December 2014 and 11 June 2015 to assess potential species influences on breakdown of crop residue and riparian organic matter sources. Crop residues were collected immediately after harvest in September 2014 from farms in Sunflower County and Coahoma County, Mississippi, USA. Residues included leaf material for maize, leaf material for cotton, and stem material for soybean. Senesced leaves of riparian species were collected in November 2014 immediately after leaf fall from trees in Lafayette County, Mississippi, USA. The leaf and crop material were air-dried after collection and stored until deployment. Litter bags were assembled by placing 10.0 g (SE = 0.6) of air-dried litter material in coarse (5 mm) mesh polypropylene non-wicketed grape bags (Glacier Valley Enterprises, Baraboo, Wisconsin, USA) to allow for macroinvertebrate and microbial colonization of plant litter. In mid-December, five litter bags of each species were attached on top of three replicate 0.91 × 1.22 m racks made from heavy gage hog wire fence and placed on top of the sediment–water interface located approximately 0.5 m below the water surface at each field site (Fig. 1C). Three bags from each site (N = 9 for each species) were taken back to the laboratory immediately after deployment (Day 0) to quantify and correct for water content, ash-free dry mass (AFDM), and handling loss (Benfield, 2006). We retrieved one bag, from each rack, for each species on days 18, 41, 119 and 183 from each bayou site. Litter bags were collected and placed in individual zip lock bags and transported on ice in a cooler back to the laboratory. Litter bags were kept cold overnight and processed the next day.

We removed leaves from mesh bags in the laboratory and rinsed over a 1-mm sieve. All litter material captured by the sieve was placed in paper bags and dried at 60°C until mass stabilized and then were weighed for dry mass. Dry mass was converted to AFDM by grinding dry leaves using a Thomas-Wiley Intermediate Mill (Thomas Scientific, Swedesboro, New Jersey, USA), and ashing a 250 mg subsample at 500°C for 1 h in a Thermo Scientific Thermolyne muffle furnace (Thermo Fisher Scientific, Waltham, Massachusettes, USA). We subtracted combusted material mass from dry mass to obtain AFDM and then calculated % OM of each sub-sampled litter pack as:
$$\% {\text{ OM }} = \, \left( {\left[ {{\text{sample DM }}{-}{\text{ sample AFDM}}} \right]/\left[ {\text{sample DM}} \right]} \right) \, \times 100,$$
and used % OM to covert DM to AFDM for field collected whole litter packs (Benfield, 2006). Initial litter mass for all bags was corrected for handling loss by subtracting the average % handling loss observed within a species.

We measured C and N concentration of litter material using a Vario Max CNS elemental analyzer (Elementar, Mt. Laurel, New Jersey, USA). We measured litter P concentration by combusting 4 ± 1 mg of the material at 500°C for 2 h followed by digesting material in 1 N hydrochloric acid for 30 min at 85°C (Halvorson et al., 2016). Following digestion, samples were diluted with deionized water and analyzed with the molybdate method (APHA, 2005) using a Turner Designs Trilogy laboratory fluorometer (Model # 7200-000) equipped with the phosphate absorbance module (Model # 7200-070; Turner Designs, Sunnyvale, California, USA). In addition to calculating molar nutrient ratios, we also examined whether changes in litter nutrient concentrations were the result of microbial immobilization of dissolved nutrients or due to leaching of C compounds by evaluating changes in total mass of N and P within plant litter bags over time (Findlay, 2010).

Data analysis

We used linear regression on natural log transformed litter mass remaining at each sampling time to estimate litter breakdown rates for all species:
$${ \ln }\left( {M_{t} } \right) = {\text{ln}}\left( {M_{0} } \right) - kt$$
where Mt is the AFDM remaining at time t, M0 is the initial AFDM remaining, and k is the exponential decay coefficient. k was calculated as a function of time in days (day−1) (Benfield, 2006). We used analysis of covariance (ANCOVA) in a linear mixed model (LMM) with time as the covariate and ln(proportion AFDM remaining) as the dependent variable to compare breakdown rates among organic matter (OM) types (all species). Models included a random effect for site to account for our split-plot experimental design (six species within each site), and repeated measures from the same sampling unit. Fixed factors included OM (maize, cotton, soybean, cottonwood, sycamore, willow oak) for the comparison of six organic matter types. All initial models included site as a fixed factor as well, but final models were simplified to present average effects of species because including site did not significantly improve the models based on Akaike Information Criterion adjusted for small sample sizes (AICc). We also used LMM to test for effects of OM type over time on organic matter nutrient ratios. We used the restricted maximum likelihood (REML) criterion to fit all LMM models (Zuur et al., 2009). We used ANCOVA with a simple linear model (LM) to evaluate scaling relationships between OM breakdown rates (k) and litter nutrient concentration and compare slopes between crop and riparian sources across study sites. Because we had four different measures of nutrient concentration (C:N, C:P, %N and %P) and five different dates we created candidate models for each date × nutrient concentration combination and evaluated models using AICc. Akaike weights (wi) which can be interpreted as the weight of evidence that model i is the best approximating model given the data and all candidate models were calculated for each model (Burnham & Anderson, 1998). We assessed assumptions of models visually with normality plots (qqnorm) and standardized residual plots across treatments. If error variances differed across levels of fixed factors, we incorporated this heterogeneity into our models by modeling variance separately among treatments with the VarIdent command (Zuur et al., 2009). When models identified significant differences in response variables between factors, we used least squared means (LStrends and LSmeans) multiple comparison tests to compare slopes or means across levels of significant factors (Lenth, 2016). LMM models and multiple comparison tests were run in nlme (Pinheiro et al., 2016) and LSmeans (Lenth, 2016) packages in R (version 2.13.1; R Project for Statistical Computing, Vienna, Austria).

Results

Bayou water chemistry

Suspended solids and nutrient concentrations were high for all three bayou sites (Table 1). Mean TSS concentrations ranged from 318.8 to 583.3 mg l−1 and mean turbidity ranged from 360 to 780.7 NTU with Cow Oak Bayou and Howden Lake having the highest and lowest values, respectively. Cow Oak Bayou had higher concentrations for all dissolved nutrients except PO4–P, which was similar to Roundaway Lake (Table 1). Cow Oak Bayou also had higher TP and TKN than Howden or Roundaway Lake (TP range 201.5–494.9 µg l−1; TKN range 1608.8–2760.8 µg l−1). Other physical and chemical characteristics including hardness, alkalinity, and DOC were similar among lakes (Table 1).
Table 1

Physical and chemical characteristics (mean ± 1 SE) from biweekly sampling between Dec 2014 and June 2015 at three agricultural bayou sample sites within the Lower Mississippi River Basin of northwest Mississippi, USA

 

Cow Oak Bayou

Howden Lake

Roundaway Lake

Hardness (mg l−1)

55.83 ± 4.17

57.50 ± 2.18

58.33 ± 8.69

Alkalinity (mg l−1)

38.17 ± 4.14

43.50 ± 2.41

39.00 ± 3.04

TSS (mg l−1)

583.33 ± 62.20

318.75 ± 27.92

417.92 ± 28.02

Turbidity (NTU)

780.67 ± 117.23

360.00 ± 82.63

439.52 ± 55.55

PO4-P (µg l−1)

45.17 ± 11.35

14.91 ± 5.41

48.48 ± 17.37

NH4-N (µg l−1)

145.58 ± 29.40

76.06 ± 25.90

69.22 ± 16.15

NO3-N (µg l−1)

568.37 ± 114.60

358.47 ± 89.14

352.06 ± 32.50

TP (µg l−1)

494.92 ± 58.22

201.53 ± 20.88

279.54 ± 45.04

TKN (µg l−1)

2760.83 ± 303.86

1741.75 ± 208.19

1608.75 ± 125.13

DOC (mg l−1)

12.63 ± 1.23

11.18 ± 0.95

11.38 ± 1.22

Breakdown rates

Breakdown rates averaged across crop residue species were more than twice as high as riparian species (k = 0.010 ± 0.002 vs. 0.004 ± 0.001 for crop residue vs. riparian, respectively). Despite differences in k among sites for some species (Table 2), the initial full ANCOVA model indicated there were no significant effects of site or site × OM source interactions (LMM ANCOVA, P > 0.05) on the breakdown rates determined by the slopes between ln(proportion AFDM remaining) and number of incubation days. A simplified model excluding these factors identified OM source as a significant factor in determining breakdown rates (LMM ANCOVA, F5,261 = 80, P < 0.0001). The six species formed three groups representing statistically significant differences in breakdown rates representing the classic litter processing continuum (Fig. 2; Peterson & Cummins, 1974). Breakdown rates for maize (0.01128 ± 0.0005) and cotton (0.0121 ± 0.0005) were fast and significantly greater than the other four species (LS trends, P < 0.05). Soybean and cottonwood breakdown rates were 0.0066 ± 0.0005 and 0.0054 ± 0.0005 representing a moderate breakdown rate that was significantly lower than maize and cotton but also greater than willow oak and sycamore (LS trends, P < 0.05). Breakdown rates for willow oak (0.0029 ± 0.0005) and sycamore (0.0031 ± 0.0005) represented the lowest range of breakdown rates and were significantly less than the other four species (LS trends, P < 0.05).
Table 2

Litter breakdown rates (mean ± SE, n = 12) for three crop residue and three riparian species across three bayous located in the Lower Mississippi River Basin

Bayou

Species

k (day−1)

R 2

Cow Oak Bayou

Maize

0.0116 ± 0.0005

0.98

 

Cotton

0.0124 ± 0.0013

0.87

 

Soybean

0.0048 ± 0.0006

0.80

 

Cottonwood

0.0039 ± 0.0003

0.94

 

Sycamore

0.0017 ± 0.0002

0.87

 

Willow Oak

0.0020 ± 0.0002

0.90

Howden Lake

Maize

0.0220 ± 0.0026

0.84

 

Cotton

0.0169 ± 0.0018

0.87

 

Soybean

0.0054 ± 0.0003

0.95

 

Cottonwood

0.0069 ± 0.0008

0.84

 

Sycamore

0.0024 ± 0.0002

0.87

 

Willow Oak

0.0030 ± 0.0002

0.91

Roundaway Lake

Maize

0.0157 ± 0.0018

0.85

 

Cotton

0.0116 ± 0.0013

0.85

 

Soybean

0.0051 ± 0.0004

0.90

 

Cottonwood

0.0050 ± 0.0004

0.92

 

Sycamore

0.0025 ± 0.0002

0.94

 

Willow Oak

0.0026 ± 0.0002

0.92

Fig. 2

Mean ± 1 SE (n = 3) organic matter breakdown rates (k in days) for crop residues (cotton, maize, and soybean) and riparian leaf litter (cottonwood, American sycamore, and willow oak) deployed in freshwater bayous within LMRB. Bars with the same letter are not statistically significantly different (P > 0.05)

Nutrient changes in plant litter

Initial ratios varied among OM types for both C:N (GLS ANOVA, F1,5 = 423.9, P < 0.0001) and C:P (GLS ANOVA, F1,5 = 174.4, P < 0.0001). Cotton and soybean had C:N and C:P ratios significantly lower and higher, respectively, than all other species investigated (Table 3) (LSmeans P < 0.05). After cotton, C:N ratios significantly increased across three species [cottonwood < willow oak < sycamore (LS means, P < 0.05)] (Table 3). Sycamore and cottonwood had statistically similar C:P ratios that were greater than cotton but lower than willow oak and soybean (Table 3) (LS means, P < 0.05). Maize C:N and C:P ratios were not different from cottonwood, willow oak, or sycamore (Table 3) (LS means, P > 0.05). Nutrient concentrations followed expected patterns based on ratios with significantly lower C and higher N and P concentrations in cotton, intermediate concentrations in riparian and maize, and higher C and lower N and P concentrations in soybean litter (Table 3) (LS means, P > 0.05).
Table 3

Initial nutrient ratios and masses (mean ± 1 SE, n = 3) for six species of organic matter

Species

C:N ratio (molar)

C:P ratio (molar)

Carbon (%)

Nitrogen (%)

Phosphorus (%)

Cotton

23.34 ± 0.84A

362.63 ± 17.45A

36.05 ± 0.31A

1.82 ± 0.0.08D

0.261 ± 0.012E

Maize

78.50 ± 9.37BCD

1495.99 ± 254.36BC

43.43 ± 0.21B

0.74 ± 0.11ABC

0.094 ± 0.015ABC

Soybean

118.97 ± 4.47E

2410.47 ± 114.15D

45.17 ± 0.12C

0.45 ± 0.02A

0.049 ± 0.003A

Cottonwood

57.08 ± 1.71B

896.74 ± 30.24B

43.09 ± 0.08B

0.89 ± 0.03C

0.125 ± 0.005C

Sycamore

73.91 ± 1.92D

777.99 ± 45.59B

48.02 ± 0.11D

0.76 ± 0.02B

0.163 ± 0.008D

Willow oak

66.24 ± 0.57C

1429.01 ± 46.61C

48.75 ± 0.04E

0.86 ± 0.01C

0.089 ± 0.003B

Letters depict significant differences among species based on least squared means (LSmeans) multiple comparison tests for each measure

Changes in ratios through time varied by species for C:N (OM × Day interaction; LMM ANOVA, F25,232 = 57.3, P < 0.0001) and C:P (OM × Day interaction; LMM ANOVA, F25,230 = 53.2, P < 0.0001). Five species, including maize, cottonwood, soybean, sycamore, and willow oak had C:N ratios that significantly decreased with time (Fig. 3A) (LS means, P < 0.05). However, cotton had consistently low C:N ratios that did not differ between study dates (Fig. 3A). Decreases in ratios with time for five out of six species was driven by overall significant increased N concentration over time (Fig. 3B) (OM × Day interaction; LMM ANOVA, F25,232 = 57.3, P < 0.0001; LS means, P < 0.05). Patterns in C:P ratios were less clear with maize C:P ratios significantly decreasing between day 0 and 41, whereas cotton C:P ratios significantly increased between day 0 and 18 (LS means, P < 0.05) (Fig. 3C). Cottonwood C:P ratios did not vary with time and soybean, sycamore, and willow oak C:P ratios increased and peaked on day 18 before significantly decreasing throughout the rest of the study (LS means, P < 0.05) (Fig. 3C). Overall P concentration was highest in cotton leaves and significantly declined by more than 2 fold over time, whereas both maize and soybean organic matter increased and cottonwood P concentration decreased slightly with time (Fig. 3D) (LS means, P < 0.05). Sycamore and willow oak P concentration decreased significantly between day 0 and 18 before significantly increasing over time (Fig. 3D) (LS means, P < 0.05).
Fig. 3

Mean ± 1 SE (n = 3) C:N molar ratios (A), percent Nitrogen (%N) (B), C:P molar ratios (C), and percent Phosphorus (%P) (D) for crop residues (cotton, maize and soybean) and riparian leaf litter (cottonwood, American sycamore and willow oak) over time from leaf litter bags incubated in freshwater bayous within LMRB. Bars with the same letter represent not statistically significantly different nutrient concentration values among dates within organic matter species (P > 0.05)

The best model explaining plant litter breakdown included C:N ratios on day 183 and litter type as variables (Table 4). Plant litter k declined with increasing C:N ratios (LMM ANCOVA, F1,13 = 30.8, P < 0.001) but slopes were significantly different between OM types (OM × C:N interaction, LMM ANCOVA, F1,13 = 7.395, P = 0.017). Plant litter breakdown rates (k) declined more quickly along a narrow window of increasing litter C:N ratios among riparian species compared to crop residue species (LS trends; P <0.05; Fig. 4).
Table 4

Best approximating Analysis of Covariance (ANCOVA) models for predicting k of different organic matter types in Lower Mississippi River Basin (LMRB) bayous while considering organic matter source (crop vs. riparian) as a factor controlling slope as determined by AICc values

Day

Nutrient concentration

Adjusted R2

AICc

Δi

w i

183

CN

0.89

11.91

0.00

0.97

119

CN

0.83

21.85

9.93

0.01

183

% N

0.80

21.89

9.98

0.01

119

% N

0.83

21.99

10.08

0.01

41

% P

0.82

22.94

11.03

0.00

18

CP

0.80

24.67

12.75

0.00

41

CP

0.80

24.94

13.02

0.00

41

CN

0.79

25.62

13.71

0.00

18

% P

0.79

25.89

13.97

0.00

18

CN

0.78

26.12

14.21

0.00

119

CP

0.78

26.34

14.43

0.00

18

% N

0.77

27.32

15.40

0.00

183

CP

0.72

27.42

15.51

0.00

119

% P

0.77

30.07

18.16

0.00

183

% P

0.68

30.07

18.16

0.00

41

% N

0.73

30.14

18.23

0.00

0

CN

0.73

30.24

18.33

0.00

0

% N

0.68

32.93

21.02

0.00

0

% P

0.65

34.98

23.07

0.00

0

CP

0.62

36.05

24.14

0.00

Intercept and error terms were also included in all models. Only models with wi ≥ 0.10 are considered to have strong evidence of support. CN Carbon:Nitrogen, CP Carbon:Phosphorus, % N % Nitrogen, % P % Phosphorus, wi Akaike weighting, Δi difference in AICc of model from the best model

Fig. 4

Relationships between log transformed organic matter breakdown rates (k in days) and leaf C:N ratios on sample day for crop residues (closed symbols; cotton, maize and soybean) and riparian leaf litter (open symbols; cottonwood, American sycamore, and willow oak) from leaf litter bags incubated in freshwater bayous within the Lower Mississippi River Basin

Discussion

High commodity crops can be the dominant vegetation in agricultural watersheds and potentially represent a significant organic matter input to streams, lakes and bayous within the LMRB and other intensively cultivated large river floodplain regions around the world. Yet little is known about their contribution to productivity within aquatic ecosystems. Currently there is a paucity of literature dedicated to understanding riparian litter breakdown in oxbow lakes and bayou ecosystems which are important water bodies within large river floodplain landscapes (Taylor et al., 2017). Field observations supported our primary hypothesis that common species of remnant floodplain forests existing along narrow margins of bayous have slower litter breakdown rates than residues from crop species cultivated within LMRB agricultural watersheds. The impact of shifts in the dominant source of organic matter inputs toward fast processing crop residues within low-gradient aquatic agroecosystems combined with earlier timing of inputs (late summer harvest) is currently unknown. Potential losses of a balanced processing continuum among plant litter species within bayou agroecosystems potentially impacts microbial and detritivore communities by creating OM deficits later in the year (Peterson & Cummins, 1974). Alternatively, in watersheds where both OM sources (crop residue and riparian) are abundant, the faster decomposing crop residue litter may provide a subsidy of conditioned organic matter earlier in the season, potentially altering aquatic communities through shifting resource subsidies (Taylor et al., 2015).

A growing body of literature suggests that high inputs of maize to aquatic ecosystems embedded within agricultural landscapes will result in overall high organic matter decomposition and a reduction in timing of available detrital base within these systems (Griffiths et al., 2009, 2012; Swan et al., 2009; Taylor et al., 2017). While breakdown rates for soybean and cotton residue have been investigated in terrestrial agricultural soils (Kalburtju & Mamolos, 2000; Lachnicht et al., 2004; Booker et al., 2005), our results provide the first reported breakdown rates for cotton and soybean residue from aquatic ecosystems. A surprising finding in our study was that despite the lack of leaf material due to using leaf defoliants as part of the harvest process, soybean stem material still broke down significantly faster than two of the three riparian species examined. Kalburtji & Mamolos (2000) found that soybean stems had high breakdown rates that were greater than rates measured for maize stalks within soils, but did not report k for leaf material. Another study reports that soybean leaf material had faster breakdown rates than stems within agricultural soils (Booker et al., 2005). Thus our estimates for soybean residue breakdown are likely conservative, given that considerable soybean biomass in the form of leaf abscission during chemical defoliation may be lost and transported to aquatic ecosystems in particulate or dissolved forms early in the natural breakdown process. Breakdown rates for cottonwood and sycamore were similar and higher, respectively, to rates reported for winter conditions in another North American lake (Ostrofsky, 1997). Our estimated breakdown rates for willow oak corresponded well with reports from an Arkansas wetland (Fuell et al., 2013).

Plant litter nutrient concentrations and lignin concentration play an important role in the initial colonization by microbes and overall breakdown rates of organic matter in aquatic ecosystems (Gessner & Chauvet, 1994; Ostrofsky, 1997; Royer & Minshall, 2001). Litter nutrient concentration explained significant variation in k in the current study, but the response was strongest based on C:N ratios at the end of the study, and when OM types were examined separately. Linkages between stream enrichment and OM nutrient concentration have been shown to strengthen with incubation time (Shaftel et al., 2011). Additionally, changes in OM nutrient ratios can be the result of microbial immobilization of dissolved nutrients and/or leaching of C compounds (Findlay, 2010). Lack of evidence for N immobilization on cotton leaf litter within bayous was similar to results reported for cotton litter decomposing within agricultural soils (Lachnicht et al., 2004). All other species in our study followed the expected pattern of N immobilization combined with C loss resulting in decreasing C:N ratios through the breakdown process (Findlay, 2010). This pattern matched observations by Taylor et al. (2017) for maize and willow oak decomposing in bayous during the spring–summer months. Overall C:N values by the end of the study represented a wide gradient for crop species and a relatively small gradient for riparian species which supports our finding of a higher slope in the relationship between breakdown rates and C:N ratios for riparian versus crop litter. Our observed differences in slope likely culminate from a combination of differences in initial C:N values (cotton which did not change) and varying degrees of N immobilization throughout the breakdown process among the other litter species suggesting linkages between N immobilization and decomposition mechanisms.

One component that this study did not consider is the possible differences in lignin concentration among OM types. Lignin concentration in crop OM can have a considerable range but is generally lower than the riparian species used in this study. Cotton lignin concentration in leaf material can range from 1.5 to 2.5% (Booker, 2000; Ververis et al., 2004). Maize leaf lignin concentration ranges from 3.2 to 4.1% (Poerschmann et al., 2005), while soybean stalk lignin concentration is about 11.5 to 18.9% (Peltier et al., 2009). In comparison, riparian leaf OM has lignin concentration greater than crop leaves. For example, reported ranges for cottonwood leaf, sycamore, and oak leaf lignin concentration are 5–25.4%, 18.2–38.9%, and 15–35.9%, respectively (Sharpe et al., 1980; Ostrofsky, 1997; Wait et al., 1999). Royer & Minshall (2001) found that lignin based measures rather than N-based measures of leaf quality were better predictors of breakdown rates among three leaf species in streams. Our relatively low sample size suggests that our findings related to litter C:N ratios should be interpreted with caution, and lignin is at the very least, likely an important co-variable with litter C:N ratios in explaining differences in organic matter breakdown rates between crop and riparian OM in agricultural bayous.

The current study also provides information for comparing seasonal differences in breakdown for maize and willow oak within bayous. Previous work on maize and willow oak within our bayou study sites demonstrated that maize broke down much more quickly than willow oak, highly productive lentic habitats had significantly greater rates than hydrologically unstable inflow sites, and experimental nutrient enrichment stimulated breakdown (Taylor et al., 2017). Breakdown rates for maize and willow oak, when incubated in bayous during the winter-spring months (\(\bar{X}\) daily temperature among sites: 12.9–14.2°C), were much lower than those reported by Taylor et al. (2017) for spring–summer months (\(\bar{X}\) daily temperature among sites: 25.2–25.69°C). Winter rates observed in this study fell within the range previously reported for maize breakdown (k = 0.0021–0.06 day−1; Griffiths et al., 2009, 2012; Swan et al., 2009). Within site comparison of maize breakdown rates indicated that winter rates were lower than observed rates from the same location in spring–summer (~ 0.03 day−1) but still above literature thresholds for classification of fast breakdown rates (> 0.01 day−1). In contrast, winter breakdown rates for willow oak in LMRB bayous can be classified as low and conform with previously reported values from LMRB wetland systems in Arkansas (Fuell et al., 2013) and a variety of oak species from a Pennsylvania lake (Ostrofsky, 1997). Previously measured spring–summer rates from the same lakes were exceptionally high for a recalcitrant oak species (> 0.01 day−1) and are similar to rates reported for Oak Creek in Arizona (Leroy & Marks, 2006; Taylor et al., 2017).

Elevated temperatures stimulate metabolism of organic material by accelerating biochemical reactions within microbes and detritivores responsible for leaf breakdown (Brown et al., 2004; Gessner et al., 1999) and sensitivity of these reactions to temperature is quantified by activation energy, Ea (Arrhenius, 1915). Metabolic theory predicts an Ea of ~ 0.65 eV if rates of litter breakdown are driven by temperature sensitivity of microbial and detritivore metabolism (Allen et al., 2005). Studies in both terrestrial and aquatic systems suggest that breakdown rates for low-quality, recalcitrant litter have higher Ea and are therefore more likely to respond to differences in temperature compared to high-quality labile litter (Fierer et al., 2005; Conant et al., 2008; Follstad Shah et al., 2017). The greater magnitude of difference in breakdown for willow oak litter compared to maize when comparing winter (current study) and spring–summer (Taylor et al., 2017) rates support these theoretical expectations and contribute to a growing understanding of how temperature influences metabolism of organic matter in aquatic systems (Fernandes et al., 2014; Griffiths & Tiegs, 2016; Follstad Shah et al., 2017). More work is needed on the timing of litter inputs combined with estmates of breakdown across a range of seasons to improve ecologists understanding of organic matter processing within aquatic ecosystems (Graça et al., 2001). This may be especially important in agricultural landscapes where both the source and the timing of organic matter inputs is potentially influenced by agricultural practices. For example Dalzell et al. (2005) observed terrestrial particulate organic matter inputs during flood conditions in an agricultural watershed to be a significant fraction of stream water total organic carbon, especially in fields with conservation tillage practices where crop residues are left on the field to improve soil organic carbon. More rapid breakdown rates at warmer temperatures also highlight potential interacting effects of future climate change scenarios and nutrient run-off on ecosystem processes within LMRB aquatic agroecosystems (Yasarer et al., 2017).

Management implications for aquatic ecosystems in intensive agricultural regions

Current agricultural best management practices focus primarily on soil erosion, nutrient transport, and pesticide loss during run-off events. Only more recently has focus shifted to include transport of agriculturally-sourced particulate organic matter into aquatic systems (Tank et al., 2010; Griffiths et al., 2012; Taylor et al., 2017). Alterations in the amount and quality of OM in rivers, lakes, and streams caused by land-use changes from bottomland hardwood forest to intensively cultivated landscapes can have direct and indirect effects on heterotrophic and autotrophic communities. Our study demonstrated that not only does maize-sourced particulate OM rapidly decompose as shown in other studies (Griffiths et al., 2009; Taylor et al., 2017), but also other major crop species such as cotton and soybean OM decompose rapidly relative to riparian species. Additionally, differences in OM nutrient concentration and concomitant relationships with breakdown rates can have significant implications for developing OM budgets, quantifying storage of C, N, and P, and understanding food web interactions within aquatic agroecosystems. For example, allochthonous subsidies comprised of early inputs of highly labile OM sources may lead to resource deficits as winter progresses, potentially altering aquatic communities that are dependent on detritus for food and shelter throughout the year. All of these factors also influence relationships among primary productivity, system respiration, and whole ecosystem metabolism, which can potentially increase system vulnerability to oxygen stress as well as alter N and P cycling and export. More research is needed to improve overall understanding of how agriculturally-sourced particulate OM influences aquatic ecosystem services with the goal of better managing water resources in highly modified large river floodplain agroecosystems.

Notes

Acknowledgements

US Department of Agriculture (USDA) Agricultural Research Service-Current Research Information System Funds supported this work. Katelynn Dillard provided significant field and laboratory assistance collecting and processing litter bags. John Massey, Terry Welch, and Duane Shaw conducted routine water-quality collections at field sites. Lisa Brooks and James Hill conducted all nutrient and water-quality laboratory analyses. Lindsey Yasarer and Jeff Back reviewed an earlier version of this manuscript. Mention of any trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity employer and provider.

References

  1. Allen, A. P., J. F. Gillooly & J. H. Brown, 2005. Linking the global carbon cycle to individual metabolism. Functional Ecology 19: 202–213.CrossRefGoogle Scholar
  2. APHA (American Public Health Association), 2005. Standard Methods for the Analysis of Water and Wastewater, 21st ed. American Public Health Association, American Water Works Association, & Water Environment Federation, Washington, DC.Google Scholar
  3. Arrhenius, S., 1915. Quantitative Laws in Biological Chemistry. Bell, London, UK.Google Scholar
  4. Benfield, E. F., 2006. Decomposition of leaf material. In Hauer, F. R. & G. A. Lamberti (eds), Methods in Stream Ecology. Academic Press, San Diego: 711–720.Google Scholar
  5. Booker, F. L., 2000. Influence of carbon dioxide enrichment, ozone and nitrogen fertilization on cotton (Gossypium hirsutum L.) leaf and root composition. Plant, Cell & Environment 23: 573–583.CrossRefGoogle Scholar
  6. Booker, F. L., S. A. Prior, H. A. Torbert & S. Hu, 2005. Decomposition of soybean grown under elevated concentrations of CO2 and O3. Global Change Biology 11: 685–698.CrossRefGoogle Scholar
  7. Brown, J., J. Gillooly, A. Allen, V. Savage & G. West, 2004. Toward a metabolic theory of ecology. Ecology 85: 1771–1789.CrossRefGoogle Scholar
  8. Burnham, K. P. & D. R. Anderson, 1998. Model Selection and Inference. Springer, Heidelberg, Germany.CrossRefGoogle Scholar
  9. Conant, R. T., R. A. Drijber, M. L. Haddix, W. J. Parton, E. A. Paul, A. F. Plante, J. Six & J. M. Steinweg, 2008. Sensitivity of organic matter decomposition to warming varies with its quality. Global Change Biology 14: 868–877.CrossRefGoogle Scholar
  10. Dalzell, B. J., T. R. Filley & J. M. Harbor, 2005. Flood pulse influences on terrestrial organic matter export from an agricultural watershed. Journal of Geophysical Research 110: G02011.CrossRefGoogle Scholar
  11. Fernandes, I., S. Seena, C. Pascoal & F. Cássio, 2014. Elevated temperature may intensify the positive effects of nutrients on microbial decomposition in streams. Freshwater Biology 59: 2390–2399.CrossRefGoogle Scholar
  12. Fierer, N., J. M. Craine, K. McLauchlan & J. P. Schimel, 2005. Litter quality and the temperature sensitivity of decomposition. Ecology 86: 320–326.CrossRefGoogle Scholar
  13. Findlay, S., 2010. Stream microbial ecology. Journal of the North American Benthological Society 29: 170–181.CrossRefGoogle Scholar
  14. Follstad Shah, J. J., J. S. Kominoski, M. Ardón, W. K. Dodds, M. O. Gessner, N. A. Griffiths, C. P. Hawkins, S. L. Johnson, A. Lecerf, C. J. Leroy, D. W. P. Manning, A. D. Rosemond, R. L. Sinsabaugh, C. M. Swan, J. R. Webster & L. H. Zeglin, 2017. Global synthesis of the temperature sensitivity of leaf litter breakdown in streams and rivers. Global Change Biology 23: 3064–3075.CrossRefGoogle Scholar
  15. Fuell, A. K., S. A. Entrekin, G. S. Owen & S. K. Owen, 2013. Drivers of leaf decomposition in two wetland types in the Arkansas River Valley, U.S.A. Wetlands 33: 1127–1137.CrossRefGoogle Scholar
  16. Gennet, S., J. Howard, J. Langholz, K. Andrews, M. D. Reynolds & S. A. Morrison, 2013. Farm practices for food safety: an emerging threat to floodplain and riparian ecosystems. Frontiers in Ecology and the Environment 11: 236–242.CrossRefGoogle Scholar
  17. Gessner, M. O. & E. Chauvet, 1994. Importance of stream microfungi in controlling breakdown rates of leaf-litter. Ecology 75: 1807–1817.CrossRefGoogle Scholar
  18. Gessner, M. O., E. Chauvet & M. Dobson, 1999. A perspective on leaf litter breakdown in streams. Oikos 85: 377–384.CrossRefGoogle Scholar
  19. Graça, M. A. S., R. C. F. Ferreira & C. N. Coimbra, 2001. Litter processing along a stream gradient: the role of invertebrates and decomposers. Journal of the North American Benthological Society 20: 408–420.CrossRefGoogle Scholar
  20. Griffiths, N. A. & S. D. Tiegs, 2016. Organic-matter decomposition along a temperature gradient in a forested headwater stream. Freshwater Science 35: 518–533.CrossRefGoogle Scholar
  21. Griffiths, N. A., J. L. Tank, T. V. Royer, E. J. Rosi-Marshall, M. R. Whiles, C. P. Chambers, T. C. Frauendorf & M. A. Evans-White, 2009. Rapid decomposition of maize detritus in agricultural headwater streams. Ecological Applications 19: 133–142.CrossRefGoogle Scholar
  22. Griffiths, N. A., J. L. Tank, S. S. Roley & M. L. Stephen, 2012. Decomposition of maize leaves and grasses in restored agricultural streams. Freshwater Science 31: 848–864.CrossRefGoogle Scholar
  23. Gulis, V. & K. Suberkropp, 2003. Leaf litter decomposition and microbial activity in nutrient-enriched and unaltered reaches of a headwater stream. Freshwater Biology 48: 123–134.CrossRefGoogle Scholar
  24. Gulis, V., V. Ferreira & M. A. S. Graça, 2006. Stimulation of leaf litter decomposition and associated fungi and invertebrates by moderate eutrophication: implications for stream assessment. Freshwater Biology 51: 1655–1669.CrossRefGoogle Scholar
  25. Halvorson, H. M., G. White, J. T. Scott & M. A. Evans-White, 2016. Dietary and taxonomic controls on incorporation of microbial carbon and phosphorus by detritivorous caddisflies. Oecologia 180: 567–579.CrossRefGoogle Scholar
  26. Hanberry, B. B., J. M. Kabrick & H. S. He, 2015. Potential tree and soil carbon storage in a major historical floodplain forest with disrupted ecological function. Perspectives in Plant Ecology, Evolution and Systematics 17: 17–23.CrossRefGoogle Scholar
  27. Jensen, P. D., G. P. Dively, C. M. Swan & W. O. Lamp, 2010. Exposure and nontarget effects of transgenic Bt corn debris in streams. Environmental Entomology 39: 707–714.CrossRefGoogle Scholar
  28. Johnson, B. R. & J. B. Wallace, 2005. Bottom-up limitation of a stream salamander in a detritus-based food web. Canadian Journal of Fisheries and Aquatic Sciences 62: 301–311.CrossRefGoogle Scholar
  29. Kalburtji, K. L. & A. P. Mamolos, 2000. Maize, soybean and sunflower litter dynamics in two physicochemically different soils. Nutrient Cycling in Agroecosystems 57: 195–206.CrossRefGoogle Scholar
  30. Kominoski, J. S., A. D. Rosemond, J. P. Benstead, V. Gulis, J. C. Maerz & D. W. P. Manning, 2015. Low-to-moderate nitrogen and phosphorus concentrations accelerate microbially driven litter breakdown rates. Ecological Applications 25: 856–865.CrossRefGoogle Scholar
  31. Lachnicht, S. L., P. F. Hendrix, R. L. Potter, D. C. Coleman & D. A. Crossley Jr., 2004. Winter decomposition of transgenic cotton residue in conventional-till and no-till systems. Applied Soil Ecology 27: 135–142.CrossRefGoogle Scholar
  32. Langhans, S. D. & K. Tockner, 2006. The role of timing, duration, and frequency of inundation in controlling leaf litter decomposition in a river-floodplain ecosystem (Tagliamento, northeastern Italy). Oecologia 147: 501–509.CrossRefGoogle Scholar
  33. Lenth, R, 2016. lsmeans: least-squares means. R Project for Statistical Computing, Vienna, Austria [available from: https://cran.r-project.org/web/packages/lsmeans]
  34. Leroy, C. J. & J. C. Marks, 2006. Litter quality, stream characteristics and litter diversity influence decomposition rates and macroinvertebrates. Freshwater Biology 51: 605–617.CrossRefGoogle Scholar
  35. Locke, M. A., D. D. Tyler & L. A. Gaston, 2010. Soil and water conservation in the Mid-South United States: lessons learned and a look to the future. In Zobeck, T. M. & W. F. Schillinger (eds), Soil and Water Conservation Advances in the United States. Soil Science Society of America, Madison, Wisconsin: 201–236.Google Scholar
  36. McCarty, J. L., S. Korontzi, C. O. Justice & T. Loboda, 2009. The spatial and temporal distribution of crop residue burning in the contiguous United States. Science of the Total Environment 407: 5701–5712.CrossRefGoogle Scholar
  37. Ostrofsky, M. L., 1997. Relationship between chemical characteristics of autumn-shed leaves and aquatic processing rates. Journal of the North American Benthological Society 16: 750–759.CrossRefGoogle Scholar
  38. Paul, M. J., J. L. Meyer & C. A. Couch, 2006. Leaf breakdown in streams differing in catchment land use. Freshwater Biology 51: 1684–1695.CrossRefGoogle Scholar
  39. Peltier, A. J., R. D. Hatfield & C. R. Grau, 2009. Soybean stem lignin concentration relates to resistance to Sclerotinia sclerotiorum. Plant Disease 93: 149–154.CrossRefGoogle Scholar
  40. Peterson, R. C. & K. W. Cummins, 1974. Leaf processing in a woodland stream. Freshwater Biology 4: 345–368.Google Scholar
  41. Pinheiro, J., D. Bates, S. DebRoy, D. Sarkar, S. Heisterkamp & B. Van Willigen, 2016. nlme: linear and nonlinear mixed effects models. R Project for Statistical Computing, Vienna, Austria [available from: https://cran.r-project.org/web/packages/nlme).
  42. Poerschmann, J., A. Gathmann, J. Augustin, U. Langer & T. Górecki, 2005. Molecular composition of leaves and stems of genetically modified Bt and near-isogenic non-Bt maize—characterization of lignin patterns. Journal of Environmental Quality 34: 1508–1518.CrossRefGoogle Scholar
  43. Rosi-Marshall, E. J., J. L. Tank, T. V. Royer, M. R. Whiles, M. Evans-White, C. Chambers, N. A. Griffiths, J. Pokelsek & M. L. Stephen, 2007. Toxins in transgenic crop byproducts may affect headwater stream ecosystems. Proceedings of the National Academy of Sciences of the United States of America 104: 16204–16208.CrossRefGoogle Scholar
  44. Royer, T. V. & G. W. Minshall, 2001. Effects of nutrient enrichment and leaf quality on the breakdown of leaves in a hardwater stream. Freshwater Biology 46: 603–610.CrossRefGoogle Scholar
  45. Rueda-Delgado, G., K. M. Wantzen & M. B. Tolosa, 2006. Leaf-litter decomposition in an Amazonian floodplain stream: effects of seasonal hydrological changes. Journal of the North American Benthological Society 25: 233–249.CrossRefGoogle Scholar
  46. Scharnweber, K., J. Syväranta, S. Hilt, M. Brauns, M. J. Vanni, S. Brothers, J. Köhler, J. Knežević-Jarić & T. Mehner, 2014. Whole-lake experiments reveal the fate of terrestrial particulate organic carbon in benthic food webs of shallow lakes. Ecology 95: 1496–1505.CrossRefGoogle Scholar
  47. Shaftel, R. S., R. S. King & J. A. Back, 2011. Breakdown rates, nutrient concentrations, and macroinvertebrate colonization of bluejoint grass litter in headwater streams of the Kenai Peninsula, Alaska. Journal of the North American Benthological Society 30: 386–398.CrossRefGoogle Scholar
  48. Sharpe, D. M., K. Cromack, W. C. Johnson & B. S. Ausmus, 1980. A regional approach to litter dynamics in Southern Appalachian forests. Canadian Journal of Forest Research 10: 395–404.CrossRefGoogle Scholar
  49. Stelzer, R. S., J. T. Scott & L. A. Bartsch, 2014. Buried particulate organic carbon stimulates denitrification and nitrate retention in stream sediments at the groundwater-surface water interface. Freshwater Science 34: 161–171.CrossRefGoogle Scholar
  50. Swan, C. M., P. D. Jensen, G. P. Dively & W. O. Lamp, 2009. Processing of transgenic crop residues in stream ecosystems. Journal of Applied Ecology 46: 1304–1313.Google Scholar
  51. Tank, J. L., E. J. Rosi-Marshall, N. A. Griffiths, S. A. Entrekin & M. L. Stephen, 2010. A review of allochthonous organic matter dynamics and metabolism in streams. Journal of the North American Benthological Society 29: 118–146.CrossRefGoogle Scholar
  52. Taylor, J. M., M. J. Vanni & A. S. Flecker, 2015. Top-down and bottom-up interactions in freshwater ecosystems: emerging complexities. In Hanley, T. C. & K. J. La Pierre (eds), Trophic Ecology: Bottom-Up and Top-Down Interactions Across Aquatic and Terrestrial Systems. Cambridge University Press, Cambridge, UK: 55–85.CrossRefGoogle Scholar
  53. Taylor, J. M., R. E. Lizotte Jr., S. Testa III & K. R. Dillard, 2017. Habitat and nutrient enrichment affect decomposition of maize and willow oak detritus in Lower Mississippi River Basin bayous. Freshwater Science 36: 713–725.CrossRefGoogle Scholar
  54. Tockner, K., D. Pennetzdorfer, N. Reiner, F. Schiemer & J. V. Ward, 1999. Hydrological connectivity, and exchange of organic matter and nutrients in a dynamic river-floodplain system (Danube, Austria). Freshwater Biology 41: 521–535.CrossRefGoogle Scholar
  55. Tundisi, J. G. & T. M. Tundisi, 2012. Limnology. CRC Press, Boca Raton, FL.Google Scholar
  56. United States Department of Agriculture, National Agricultural Statistics Service, 2015. Agricultural Statistics 2015. United States Government Printing Office, Washington, DC.Google Scholar
  57. Ververis, C., K. Georghiou, N. Christodoulakis, P. Santas & R. Santas, 2004. Fiber dimensions, lignin and cellulose content of various plant materials and their suitability for paper production. Industrial Crops and Products 19: 245–254.CrossRefGoogle Scholar
  58. Wait, D. A., C. G. Jones, J. Wynn & F. I. Woodward, 1999. The fraction of expanding to expanded leaves determines the biomass response of Populus to elevated CO2. Oecologia 121: 193–200.CrossRefGoogle Scholar
  59. Wallace, J. B., S. L. Eggert, J. L. Meyer & J. R. Webster, 1999. Effects of resource limitation on a detrital-based ecosystem. Ecological Monographs 69: 409–442.CrossRefGoogle Scholar
  60. Webster, J. R. & E. F. Benfield, 1986. Vascular plant breakdown in freshwater ecosystems. Annual Review of Ecology and Systematics 17: 567–594.CrossRefGoogle Scholar
  61. Yasarer, L. M. W., S. Sinnathamby & B. S. M. Sturm, 2016. Impacts of biofuel-based land-use change on water quality and sustainability in a Kansas watershed. Agricultural Water Management 175: 4–14.CrossRefGoogle Scholar
  62. Yasarer, L. M. W., R. L. Bingner, J. D. Garbrecht, M. A. Locke, R. E. Lizotte Jr., H. G. Momm & P. R. Busteed, 2017. Climate change impacts on runoff, sediment, and nutrient loads in an agricultural watershed in the Lower Mississippi River Basin. Applied Engineering in Agriculture 33: 379–392.CrossRefGoogle Scholar
  63. Zuur, A. F., E. N. Ieno, N. Walker, A. A. Saveliev & G. M. Smith, 2009. Mixed effects models and extensions in ecology with R. Springer, New York.CrossRefGoogle Scholar

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply  2018

Authors and Affiliations

  1. 1.Water Quality and Ecology Research Unit, National Sedimentation LaboratoryUnited States Department of Agriculture, Agricultural Research ServiceOxfordUSA

Personalised recommendations