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Acoustical and nematode community assessment for ecosystem characterization

ProQuest Dissertations and Theses, 2009
Dissertation
Author: Marisol Andrea Quintanilla Tornel
Abstract:
Ecosystems with different levels and types of human management were compared and characterized using nematode community structure methodologies and acoustical recordings. Conventional tillage, no-till, bio-based (organic), early and mid-successional fields with a history of tillage and mature deciduous forest sites were compared using these two methodologies. These methodologies were used to characterize the biological and physical aspects of these ecosystems with different levels of human management. For the nematode community structure analysis, nematodes naturally found in the soils from the conventional tillage, no-till, bio-based, successional fields and deciduous forest systems from Kellogg Biological Station Long Term Ecological Research (KBS/LTER) were identified to the lowest possible taxon and the results analyzed for taxon biodiversity, evenness, ecosystem stability and nutrient enrichment. Multivariate canonical correspondence analyses were performed in order to find associations between nematode taxa, ecosystems and soil characteristics. The results were compared and images of the nematodes identified can be found at http://www.nemasoil.com. As expected, the greatest biodiversity, evenness and ecosystem stability were often found in the deciduous forest and field succession ecosystems, and the lowest levels of the same parameters were most often found in the no-till and conventionally-tilled ecosystems. The acoustical methodologies were used to measure both biological and physical characteristics of ecosystems. The physical characteristics, such as soil aggregate stability, of soils with different levels of human management were measured with acoustical methods and the results were compared to conventional methods. Soil aggregates from conventional tillage, no-till, and native systems were immersed in water and the sounds of the rapid hydration were recorded with hydrophones for 30 seconds. The results were as expected and agreed with conclusions arrived at using conventional methods. The greatest stability and lowest sound intensity was found in the native ecosystems and the greatest sound intensity and lowest aggregate stability was found in the conventionally-tilled ecosystems, with the no-till ecosystems being intermediate. Acoustical recordings were used additionally to characterize the biological insect sounds from the same ecosystems used previously at KBS/LTER. The native ecosystems had significantly more insect sounds, especially at night, compared to any of the human-managed ecosystems. In the native ecosystems, the sounds were in a wave cubic regression form, with the sounds increasing and decreasing at regular time intervals. Additionally, acoustics were used to determine the biological activity of compost, using water as an activating agent. In conclusion, our methods showed that both nematode community structure methods and acoustical methods can be effectively used to characterize ecosystems.

TABLE OF CONTENTS LIST OF TABLES vii LIST OF FIGURES x INTRODUCTION 1 CHAPTER 1: IMPACT OF TILLAGE ON ACOUSTICAL SIGNATURES DURING RAPID HYDRATION OF AIR-DRIED SOIL AGGREGATES 6 Abstract 6 Introduction 7 Materials and Methods 11 Results 16 Discussion 20 Conclusions 30 Tables 32 Figures 35 References 42 CHAPTER 2: NEMATODE COMMUNITY STRUCTURE OF SOIL FROM ALTERNATIVE MANAGEMENT AND NATURAL ECOSYSTEMS 46 Abstract 46 Introduction 48 Materials and Methods 51 Results 55 Discussion 68 Tables 90 Figures 113 References 121 CHAPTER 3: TEMPORAL DYNAMICS OF ACOUSTICAL SIGNATURES ASSOCIATED WITH ALTERNATIVE MANAGEMENT AND NATURAL ECOSYSTEMS 131 Abstract 131 Introduction 132 Materials and Methods 136 Results 138 Discussion 142 Tables 146 Figures 147 References 164 v

CHAPTER 4: IMPACT OF MICROWAVES AND WATER ON ACOUSTICAL SIGNATURES OF A COMPOST 166 Abstract 166 I ntroduction 167 Materials and Methods 169 Results 171 Discussion 171 Tables 174 Figures 175 References 179 CONCLUSION 183 APPENDICES 188 VI

LIST OF TABLES Table 1.1 Soil collection locations, managements, carbon (%) and bulk density of the six soil air-dried aggregates sets used in this research 32 Table 1.2 Soil collection locations, site establishment dates, management descriptions, and taxonomy of the of the six soils aggregate sets used in this research 33 Table 1.3 Matlab code for computation of the average Power Spectral Density (acoustical intensity) for 1 kHz frequency bins (plus total) from a .wav file segment 34 Table 2.1 Description and abbreviations for the ecosystems used in this dissertation. The ecosystems are in the Kellogg Biological Station Long Term Ecological Research (KBS/LTER) project, Hickory Corners, Michigan. Each replication plot consists of one hectare. The agricultural and the old field succession established in 1989 (early succession) have six replications each (six one-hectare plots). The deciduous forest and the mid-succession (KBS/LTER treatment SF) have three one hectare replications each 90 Table 2.2 Nematode community structure indicator formulae used for descriptive, comparative and temporal analysis of ecosystem biodiversity, evenness, abundance, maturity, and enrichment of six ecosystems at KBS/LTER, Hickory Corners, Michigan 91 Table 2.3 Nematode taxa recovered from the deciduous forest ecosystem at KBS/LTER, Hickory Corners Michigan. Table contains nematode Taxa, Oligocheates and Tardigrades densities and frequencies in 29 samples from three deciduous forest sites at KBS/LTER 93 Table 2.4 Mid-succession (abandoned in ~1967) ecosystem characterization consisting of: Nematode Taxa, Oligocheates and Tardigrades densities and frequencies in 4 samples from succession sites at KBS/LTER, Hickory Corners, Michigan 96 Table 2.5 Early succession (last tilled in spring of 1989 and occasional burning) ecosystem characterization consisting of: Nematode Taxa, Oligocheates and Tardigrades densities and frequencies in 12 samples from six field old field succession sites at KBS/LTER, Hickory Corners, Michigan 98 vii

Table 2.6 No Till system characterization consisting of: Nematode Taxa, Oligocheates and Tardigrades densities and frequencies in 18 samples from six no-till cropping sites at KBS/LTER, Hickory Corners, Michigan 101 Table 2.7 Bio-based system characterization consisting of Nematode Taxa, Oligocheates and Tardigrades densities and frequencies in 18 samples from six bio-based agricultural cropping sites at KBS/LTER, Michigan 103 Table 2.8 Conventional Tillage system characterization consisting of: Nematode Taxa, and Oligocheates densities and frequencies in 18 samples from six conventionally tilled sites at KBS/LTER, Hickory Corners, Michigan 106 Table 2.10 Index of similarity (Sorensen 1948). Based on the presence or absence of taxa in six ecosystems at KBS/LTER, Hickory Corners, Michigan.. 108 Table 2.11 Nematode community structure, biodiversity, evenness and ecosystem maturity (Table 2) analysis table for September 25, 2007 sample date at KBS/LTER with means, standard errors (SE), analysis of variance (AOV) and non-parametric mood median (MM) test 109 Table 2.12 Nematode community structure and biodiversity (Table 2) analysis Table for December 12, 2007 sampling date at KBS/LTER with means, standard errors (SE), and analysis of variance (AOV) 110 Table 2.13 Nematode Community Structure and Biodiversity (Table 2) Analysis Table for September 25, 2008 sampling date at KBS/LTER: with Means, Standard Errors (SE), and Analysis of Variance (AOV) 111 Table 2.14 Mean daily temperatures and precipitation for Kellogg Biological Station, Hickory Corners Michigan for the two weeks prior to three soil sampling dates 112 Table 3.1 Sound recording sampling times, dates and sunset, dusk, dawn and sunrise times at Kellogg Biological Station/Long Term Ecological Research, Michigan in 2007 146 Table 4.1 Matlab code to compute average Power Spectral Density (PSD, watts/kHz) for 1 kHz frequency bins (plus total) from a .wav file segment. The last calculation (Tab (i, 12)) is the sum for all 11 kHz levels 174 viii

LIST OF FIGURES Figure 1.1. Organization and objectives model of the Dissertation of Marisol Quintanilla Figure 1.1 a-d: Spectrograms of rapid water hydration of soil aggregates from three different soils: a) Hoytville, Ohio continuous till, b) Wooster, Ohio native forest, c) KBS, Hickory Corners, Michigan native soil and d) background sound with no soil aggregates 35 Figure 1.2 Relationship among Power Spectral Density (acoustical intensity, sum of kHz frequency levels 3-11) of soil aggregates from three soil/management systems: a native succession at the Kellogg Biological Station (KBS), Hickory Corners; Michigan, a conventionally-tilled soil from Hoytville, Ohio; and a native forest soil from Wooster, Ohio. Samples were immersed in water (H20), 50 g/L Na-Hexametaphosphate (NaHmP), or 5mM CaS04. Error bars indicate standard error of the mean of four replicate aggregates 36 Figure 1.3 Relationship between Power Spectral Density Mean (acoustical intensity, sum of kHz 2-11 frequency levels) of rapidly hydrated (immersed in water) soil aggregates and six soil/management systems: native succession at KBS (Hickory Corners, Michigan); conventionally tilled and no tilled soil from Hoytville (Ohio); and native forest, no-till and conventionally tilled soil from Wooster (Ohio). Error bars indicate standard error intervals from the mean of 30 replicates. Letters represent significant difference using 95 % confidence intervals for the mean 37 Figure 1.4 Relationship between Power Spectral Density (acoustic intensity), frequency (kHz level 2 - kHz level 11) and six soil/management systems (Hoytville Ohio tilled and no-till, KBS, Hickory Corners Michigan native soil and Wooster Ohio native, no-till and tilled soil) 38 Figure 1.5 Relationship between visual assessment of soil aggregate slaking in water (Relative Stability: 1.0= no slaking and 6.0= complete disintegration when immersed for 30 seconds). Graphs bars represent the means (soil/management systems= 6, replications=30) and 95% confidence intervals 39 Figure 1.6 Relationship between air bubbler tube size (1 = small 0.3mm, 5= large 1cm) and Power Spectral Densities (sound intensity) from kHz 1-3 levels sound frequency produced by air bubbles in water. KHz 4-11 had low values and could not be seen in the graph 40 IX

Figure 1.7 Relationship between air bubbler tube size (1 = small 0.3mm, 5= large 1cm) and Power Spectral Density (sound intensity). Air bubbles of differing sizes were released in water and the sounds recorded 41 Figure 2.1 Canonical Correspondence Analysis Biplot. The sites are found in Kellogg Biological Station, Hickory Corners, Michigan. The sites were conventionally tilled, no-till and no-input (bio-based) agricultural ecosystems, deciduous forest, early and mid-succession fields. The variables represent percent soil elemental carbon (C), percent soil elemental nitrogen (N), soil bulk density (BD) and pH 113 Figure 2.2 Nematodes per 100 cm3 (100 cc) of soil, and standard error intervals for trophic groups (Ba=bacterievores, Ca= Carnivores, Fu=fungivores, He=Herbivores, Om=Omnivores, UnlD=Unidentified nematodes), in five ecosystems at KBS/LTER, Michigan. Soil sampled September 25, 2007.... 116 Figure 2.3 Mean Number of Nematodes per 100 cm3 (100 cc) of soil, separated by trophic group (Ba=bacterievores, Ca= Carnivores, Fu=fungivores, He=Herbivores, Non-Nem=Non-Nematodes (Oligocheates and Tardigrades), Om=Omnivores, UnlD=Unidentified nematodes) on December 12, 2007. Error bars represent standard error from the mean 117 Figure 2.4 Mean Number of Nematodes per 100 cm3 of soil, and standard errors for trophic groups (Ba=bacterievores, Ca= Carnivores, Fu=fungivores, He=Herbivores, Om=Omnivores, Non-Nem=Non-Nematodes (Oligocheates and Tardigrades), and UnlD=Unidentified nematodes), in five ecosystems at KBS/LTER, Michigan. Soil sampled September 25, 2008 118 Figure 2.5 Mean Number of Nematodes per 100 cm3 of soil, and standard errors for trophic groups (bacterievores, carnivores, fungivores, herbivores, omnivores, non-nematodes (Oligocheates and Tardigrades), and unidentified nematodes), in five ecosystems (deciduous forest, succession abandoned last tilled circa 1955 (mid-succession) and last tilled in 1989 (early succession), and no-till, bio-based (organic) and conventionally tilled agricultural cropping systems) at KBS/LTER, Michigan. Three sampling dates combined September 25, 2008, December 12, 2007 and September 25, 2008 119 Figure 2.6 Nematode families associated with five soil systems at KBS/LTER, Michigan in 2007 and 2008 120 Figure 3.1 Aerial image of the Kellogg Biological Station/Long Term Ecological Research site at Hickory Corners, Michigan (http://www.lter.kbs.msu.edu/maps/images/allLTERsites.jpg). Treatments (T) used: T1= conventional tilled crop rotation, T4= no-input (organic) crop rotation, T7= early succession last tilled in 1989 and occasionally burned, SF= mid- x

succession last tilled circa 1955, DF= mature deciduous forest, CF = mature coniferous forest 147 Figure 3.2 Photographic image of recording equipment used for acoustical recordings 148 Figure 3.3 Acoustical recording equipment in the deciduous forest, Kellogg Biological Station, Hickory Corners, Michigan 148 Figures 3.4a-c Acoustic characterization sonograms of deciduous forest, early succession, and conventionally tilled corn/soybean/wheat crop rotation at 21:20, August 29, 2007. The sounds were recorded at Kellogg Biological Station, Michigan. The x axis represents 0-30 seconds in time and the y axis represents frequencies of 0-11 kHz. Color intensity indicates sound intensity. Cool blues and greens indicate lowest intensity, yellow medium and the highest sound intensity is orange, and red 149 Figure 3.5 Total power spectral density (sum power spectral density (PSD) of the frequency levels 2-11 kHz) for recordings of July and August 2007 during sunrise, noon, sundown, and midnight at the Kellogg Biological Station/Long Term Ecological Research Station, Hickory Corners, Michigan. Error represents 95% confidence interval from the mean 150 Figure 3.6 Total sound power spectral density (PSD) of frequencies 1-11 kHz for August 22-26, 2005 sound recordings at the Kellogg Biological Station, Long Term Ecological Research station at Hickory Corners, Michigan. The ecosystems recorded were: deciduous forest, coniferous forest, no-input (organic) crop rotation, and conventionally tilled crop rotation. Errors represent 95 % confidence intervals from the mean 151 Figure 3.7 Deciduous forest total sound power spectral density (PSD watt/kHz plot of the frequency levels 2-11 kHz) for recordings of July and August 2007 from 20:00-22:20 at the Kellogg Biological Station/Long Term Ecological Research Station, Hickory Corners, Michigan. Each dot represents the sound PSD of one sample. The sounds PSD increase exponentially as the evening progresses in late August and early September 152 Figure 3.8 Deciduous forest total sound power spectral density (sum power spectral density (PSD watt/kHz) plot of the frequency levels 2-11 kHz) for recordings of July and August 2007 from 6:00-00:00 at the Kellogg Biological Station/Long Term Ecological Research Station, Hickory Corners, Michigan. Each dot represents the sound PSD of one sample. The sounds PSD increase exponentially as the evening progresses in late August and early September. The sounds are close to 0 in the morning and noon in both July and August... 153 XI

Figure 3.9 Deciduous forest total sound power spectral density (sum power spectral density (PSD watt/kHz) plot of the frequency levels 2-11 kHz) for recordings of July and August 2006 from 6:00-00:00 at the Kellogg Biological Station/Long Term Ecological Research Station, Hickory Corners, Michigan. Each dot represents the sound PSD of one sample. The sounds PSD increase exponentially as the evening progresses in late August and early September. The sounds are close to 0 in the morning and noon in both July and August... 154 Figure 3.10 Cubic regression fit of the relationship of time and sound power spectral density (PSD) in the mid-succession with history of tillage. The recordings were done in the evenings of July 23 and August 22, 2007. For mid- succession with history of tillage in 2007, the sound produced in July 23, 2007 were significantly different from those of August 22, 2007 (p = 0.001)/ At both dates, time effectively (p< 0.001) predicted the insect sound intensity. The later in the evening the louder the sounds (regression analysis, analysis of variance p < 0.001, R2= 53.7%) 155 Figure 3.11 Cubic regression fit of the relationship between time and sound Power Spectral Density (PSD) in a mid-succession field last tilled in circa 1955. The sounds were recorded August 22, 2007 156 Figure 3.12 Three dimensional plot of the sum sound Power Spectral Density (PSD) of frequency levels 2-11 kHz. In 2007, the sounds of four ecosystems were recorded: deciduous forest, mid-succession field last tilled in 1955, early succession last tilled in 1989 (occasional burning) and a conventionally tilled corn/soybean/wheat rotation field at Kellogg Biological Station, Michigan 157 Figure 3.13 Distribution of sound power spectral density (PSD) in the frequency levels 1-6 kHz recorded in deciduous forests at Kellogg Biological Station, Hickory Corners, Michigan. The sounds were recorded in July and August of 2007. Sound power spectral density in the frequency levels 7-11 kHz was not different from 0, so they are not included in the graph. Errors represent 95 % confidence intervals from the mean 158 Figure 3.14 2007 sound power spectral density (PSD) of kHz frequencies 1-5 recorded in mid-successional field last tilled circa 1955 in Kellogg Biological Station, Hickory Corners, Michigan. The recordings were done between ~20:00 and 22:20 in July and August 2007. Sound power spectral density in the frequency levels 6-11 kHz was not different from 0, so they are not included in the graph. Errors represent 95 % confidence intervals from the mean 159 Figure 3.15 2007 sound power spectral density (PSD) of frequency levels 1-11 kHz recorded in early successions field occasionally burned and last tilled in 1989 at the Kellogg Biological Station, Hickory Corners, Michigan. The xii

recordings were done between ~20:00 and 22:00 in August 2007. Errors represent 95 % confidence intervals from the mean 160 Figure 3.16 Sonogram of Deciduous forest sounds in August 22, 2007, 10:15 pm. Color intensity means greater sound intensity (blue/green = low or no sound, red/maroon = very intense sound, or high PSD) 161 Figure 3.17 Sonogram of deciduous forest sounds in August 30, 2007 at 7:05 am. The insect sounds are the blurry line in kHz 4-5. The bird sounds, are seen as complex communication patterns and repetition of sounds. More sonograms can be seen in www.nemasoil.com 162 Figure 3.18 Sonograms of the sounds from an early succession with occasional burning last tilled in 1989. The recordings were done August 29, 2007 at 20:55. 163 Figure 4.1 Sonograms of sounds of non-microwaved and microwaved compost with and without water as an activating agent 175 Figure 4.2 Power Spectral Densities (PSD, sum of kHz frequency levels 1-11) of microwaved and non-microwaved compost, without and with water (p< 0.0005), with 95% confidence interval bars for the means 176 Figure 4.3 Comparison of Power Spectral Densities (PSD, kHz levels 1-5) of microwaved (MC) and non-microwaved (C) compost with (+w) or without water (- w) added as an activating agent. The four treatments where: microwaved compost with no water added (MC-w), microwaved compost with water added (MC+w), compost with no water added (C-w), and compost with water added (C+w). Each treatment had five repetitions; the bars represent the mean for the five repetitions. The error bars represent 95% confidence intervals for the means. Each kHz frequency level represented is shown separate, i.e. kHz level 1 is named khz-L1 on the graph 177 Figure 4.4 Comparison of mean relative Power Spectral Density (PSD, watts/kHz) for four treatments in the kHz frequency levels 2-11. The four treatments were: microwaved compost with no water added (MC-w), microwaved compost with water added (MC+w), compost with no water added (C-w), and compost with water added (C+w). Each treatment had five repetitions. Each kHz level represented is shown separate, i.e. kHz level 2 is named kHz-2 on the graph. The error bars represent 95% confidence intervals (CI) for the means 178 xiii

INTRODUCTION The objective of this dissertation was to obtain a greater understanding of the effect of agronomic disturbance on physical and biological aspects of ecosystems (Fig. 1.1). The work also compared the characteristics of native forest and old field successions, with agricultural no-till, no-input and conventionally tilled crop systems. The research was performed at the Michigan State University, W. K. Kellogg Biological Station, Long Term Ecological Research (KBS/LTER) in Hickory Corners, Michigan. Soil aggregates for acoustic research were collected at KBS/LTER and also obtained from Wooster, Ohio and Hoytville, Ohio. The research was divided into four mayor parts: 1) Impact of tillage on acoustical signatures during rapid hydration of air-dried soil aggregates, 2) Nematode community structure of soil from alternative management and natural ecosystems, 3) Temporal dynamics of acoustical signatures associated with alternative management and natural ecosystems, and 4) Impact of microwaves and water on acoustical signatures of a compost. Soil physical characteristics were measured through acoustical methods (Chapter 1). The sound produced by soil aggregates during the slaking process (breaking and bubbling when immersed in water) were recorded, analyzed and compared to traditional methods of water-aggregate stability measurements. The complete data set for these analyses can be found at www.nemasoil.com. Measuring of water-aggregate stability through acoustics had not been reported in the literature at the time of this dissertation. Water aggregate stability is an important soil characteristic that is related to soil quality. Soils with high 1

proportion of water-stable aggregates are less vulnerable to erosion, leaching, and soil organic matter loss. Soils with high organic carbon tend to have greater resistance to slaking (Zaher et al. 2005). Soils resistant to slaking also perform many ecosystem services more efficiently than soils with low water-aggregate stability. Management practices, such as tillage, affect water-aggregate stability (Park and Smucker 2005). The biological characteristics of the KBS/LTER ecosystems were also studied. The nematode community structures of soils from alternative managements and natural ecosystems at KBS/LTER were investigated. Soil ecosystem biodiversity, evenness, maturity, and structure are can be calculated using nematode communies (Bongers 1990, Bongers & Ferris 1999). A relationship between nematode community structure and soil quality has been reported in the literature (Neher 2001 and Bird & Birney 1998). Images and descriptions of the nematodes, Oligocheates and Tardigrades recovered from the various ecosystems at KBS/LTER can be found at www.nemasoil.com. Comparison of systems yields, soil bulk density, pH, soil carbon and nitrogen (%) can be found in appendices 26-31. Sounds can also be used to determine the biological health of ecosystems (Gage et al. 2001). Krause and Gage (2003) stated that "A landscape's acoustics signature is a unique component of the evaluation of its function". The objective of the research reported in Chapter 3 is to measure effects of ecosystem disturbance (agricultural and intensity of management) type using acoustical procedures through comparison with non-managed systems. 2

Acoustical methods do not require destructive sampling and can be done relatively easily and inexpensively, compared to other procedures. One of the reasons for this research is to enhance our understanding of ecosystem disturbance. The sounds of forest, old field successions, and no-till and no-input crops agricultural fields are reported and analyzed. The acoustical temporal dynamics of the various ecosystems were studied. It was hypothesized that the reason no significant sound intensity from biological activity at the soil surface in soils from any of the KBS/LTER ecosystems, was because of a lack of a specific activation event, or low population density of invertebrates on the soil near the recording hydrophone. Soil organisms tend to be in a dormant state and need to be activated in order to function to their full potential (Lavelle et ai, 1995). In order to test this, water was added to soil/compost with and without living invertebrates, the resulting sounds were recorded. Increase of soil insect acoustics following an activation event have been reported by Mankin et al. 2006. The effects of water as a pulsing agent over the activity and acoustics of soil/compost invertebrates were reported in chapter 4. 3

Desired System Response: A greater understanding of the effect of disturbance (focus on agronomic) on the physical and biological aspects of ecosystems; and to compare native forest and field succession ecosystems with field crop agronomical ecosystems (no- till, no-input (organic) and conventional till). Chapter 1. Effect of tillage on water-aggregate stability and resistance to slaking on soil aggregate measured through acoustical methods. Soils from KBS/LTER and Ohio Experimental Stations. Chapter 2. Nematode Community Structure analysis of native forest, field successions and agricultural cropping systems at KBS/LTER Chapter 3. Above ground acoustical insect recordings in native forest, field succession and agricultural cropping systems at KBS/LTER Chapter 4. Effect of water as a pulsing agent on the acoustics of compost Figure 1.1. Organization and objectives model of the Dissertation of Marisol Quintanilla Tornel

REFERENCES Bird, George W. and M.F. Berney. 1998. Relationship Between Nematode Community Structure and Soil Quality. KBS/LTER Meetings. http://lter.kbs.msu.edu/Meetings/1998_AILInv_Meeting/bird.htm. Bongers, T. 1990. The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia 83:14-19. Bongers, T. and H. Ferris. 1999. Nematode community structure as a bio- indicator in environmental monitoring. Trends in Ecology and Evolution 14:224- 228. Gage, S. H., B. M. Napoletano, and M. C. Cooper. 2001. Assessment of ecosystem biodiversity by acoustic diversity indices. The Journal of the Acoustical Society of America 109: 2430. Krause, B. and S. Gage. 2003. Testing Biophony as an Indicator of Habitat Fitness and Dynamics. SEKI Natural SoundScape Vital Signs Pilot Program Report, February 3. Lavelle, P.C. Lattaud, D. Trigo, and I. Barois. 1995. Mutualism and biodiversity in soils. In book by: Collins, H.P., G. P. Robertson and M. J. Klug (eds.). 1995. The Significance and Regulation of Soil Biodiversity. Kluwer Academic Publishers, Dordrecht, the Netherlands. 23-33 Neher, D.A. 2001. Role of nematodes in soil health and their use as indicators. Journal of Nematology 33: 161-168. Mankin, R. W. 2006. Increase in acoustic detectability of Plodia interpunctella larvae after low-energy microwave radar exposure. Florida Entomologist 89: 416-418. "^[45 kB] Park, E.J. and A. J. M. Smucker. 2005. Erosive strengths of concentric regions within soil macroaggregates. Soil Science Society of America Journal 69: 1912- 1921. Zaher, H., J. Caron, and B. Ouaki. 2005. Modeling aggregate internal pressure evolution following immersion to quantify mechanisms of structural stability. Soil Science Society of America Journal 69: 1-12. 5

CHAPTER 1 IMPACT OF TILLAGE ON ACOUSTICAL SIGNATURES DURING RAPID HYDRATION OF AIR-DRIED SOIL AGGREGATES ABSTRACT The objective of this research was to determine if sound can be used to discriminate among soils and soil managements. Air-dried soil aggregates were immersed in water, CaS04, or Sodium Hexametaphosphate solutions. Sounds from each aggregate hydration were digitally recorded using a hydrophone. Sounds were analyzed using Matlab software to produce sonograms and bar charts of frequency distributions. Soil aggregates were obtained from a native ecosystem at Kellogg Biological Station (Michigan); forest, tilled and non-tilled agricultural sites at the Wooster, Ohio Agricultural Experiment Station and tilled and non-tilled agricultural sites in Hoytville (Ohio). Sounds recorded from tilled soil aggregates had significantly greater sound Power Spectral Density (PSD) and variability than sounds from soils from non- managed ecosystems. We concluded that tilled soils contain a mixture of stable and less stable aggregates. Soil aggregates from tilled soils are generally less stable and contain less carbon than no-till and non-managed soils. Air released during wetting and slaking appears to cause greater sound in tilled soil than either the native soils or no- tilled soils. Either slow absorption of water or slow release of air resulted in lower PSD in aggregates from no-till and native, compared with the tilled soils. This is probably caused partly by organic matter that reduces the rate of entry of water into soil aggregates and reduces pressure buildup inside aggregates. This results in less breaking and bubbling, therefore less noise. Use of sound to record and quantify soil characteristics can be a useful way to evaluate resistance to slaking. Abbreviations: KBS/LTER, Kellogg Biological Station/Long Term Ecological Research, Michigan; NaHMP, Na-Hexametaphosphate; PSD, Power Spectral Density. 6

Soils with the ability to resist degradation and respond to management in an optimal manner usually contain stable aggregates that are not degraded by the actions of water and external mechanical stresses (Dexter 1988). The process of aggregation takes place when particles of mineral matter are joined together by various organic and inorganic substances. The numerous biological, chemical, and physical components contributing to the formation of stable soil aggregates are impacted by ecosystem disturbances, including the agricultural practice of tillage (Amezketa 1999, Six etal. 1998, Pikul et a/2009). Recently, acoustical transmission analyses have been used to identify specific soil properties (Grift et al. 2005; Moore and Attenborough 1992). Soil Acoustics The physical characteristics of soil have been measured by active acoustics (sending sounds into a soil and receiving the signal) and passive acoustics (recording sound). The National Center for Physical Acoustics has a website section dedicated to active acoustical assessment of soil (http://www.oiemiss.edu/depts/ncpa/PorMat/SA.htm). Soil characteristics such as air-filled porosity and relative air permeability have been effectively measured through acoustic propagation (Moore and Attenborough 1992). Passive acoustics have been tested to measure soil compaction (Grift et al. 2005). Additional studies on the acoustics of soil physical characteristics, including acoustics of soil aggregate hydration, are limited. 7

Soil Aggregation, Stability and Slaking Soil aggregation processes include both the formation and stabilization of aggregates. Flocculation of clay, wetting, drying, freezing, thawing, and the dynamics of both microbes and roots play important roles in aggregation. In addition, inorganic stabilizing agents like Ca2+, Al3+, Fe3+, oxides, and hydroxides of aluminum and iron, and carbonates of magnesium and calcium are involved in the process. Persistent, intermediate, and transient organic stabilizing agents may also have a role in the process. Soil organic matter can also be protected from decomposition inside an aggregate. When the aggregate breaks, the protected organic matter is subjected to microbial decomposition, releasing C02 (Jastrow and Miller 1998). Soil aggregation is crucial for preventing erosion, increasing water infiltration and maintaining soil surface integrity (Franzluebbers et al. 2000). Stability of soil aggregates, in part, determines nutrient and pollutant leaching, mineralization of nutrients, and the extent and rate of conversion of soil carbon into CO2. Soil aggregate stability is one of the physical characteristics of soil that can serve as an indicator of soil quality (Arshad and Coen 1992, Hortensius and Welling 1996). Additionally, soil properties such as erosive and crusting potential can be estimated when soil aggregate stability is measured (Amezketa 1999). Models of soil aggregation have been developed by Oades and Waters (1991), Elliot (1986), Tisdall and Oades (1982), and Edwards and Bremmer (1967); and reviewed by Amezketa (1999). 8

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Abstract: Ecosystems with different levels and types of human management were compared and characterized using nematode community structure methodologies and acoustical recordings. Conventional tillage, no-till, bio-based (organic), early and mid-successional fields with a history of tillage and mature deciduous forest sites were compared using these two methodologies. These methodologies were used to characterize the biological and physical aspects of these ecosystems with different levels of human management. For the nematode community structure analysis, nematodes naturally found in the soils from the conventional tillage, no-till, bio-based, successional fields and deciduous forest systems from Kellogg Biological Station Long Term Ecological Research (KBS/LTER) were identified to the lowest possible taxon and the results analyzed for taxon biodiversity, evenness, ecosystem stability and nutrient enrichment. Multivariate canonical correspondence analyses were performed in order to find associations between nematode taxa, ecosystems and soil characteristics. The results were compared and images of the nematodes identified can be found at http://www.nemasoil.com. As expected, the greatest biodiversity, evenness and ecosystem stability were often found in the deciduous forest and field succession ecosystems, and the lowest levels of the same parameters were most often found in the no-till and conventionally-tilled ecosystems. The acoustical methodologies were used to measure both biological and physical characteristics of ecosystems. The physical characteristics, such as soil aggregate stability, of soils with different levels of human management were measured with acoustical methods and the results were compared to conventional methods. Soil aggregates from conventional tillage, no-till, and native systems were immersed in water and the sounds of the rapid hydration were recorded with hydrophones for 30 seconds. The results were as expected and agreed with conclusions arrived at using conventional methods. The greatest stability and lowest sound intensity was found in the native ecosystems and the greatest sound intensity and lowest aggregate stability was found in the conventionally-tilled ecosystems, with the no-till ecosystems being intermediate. Acoustical recordings were used additionally to characterize the biological insect sounds from the same ecosystems used previously at KBS/LTER. The native ecosystems had significantly more insect sounds, especially at night, compared to any of the human-managed ecosystems. In the native ecosystems, the sounds were in a wave cubic regression form, with the sounds increasing and decreasing at regular time intervals. Additionally, acoustics were used to determine the biological activity of compost, using water as an activating agent. In conclusion, our methods showed that both nematode community structure methods and acoustical methods can be effectively used to characterize ecosystems.