Monitoring Agroecosystem Biodiversity Using Bioacoustics and Remote Recording Units
Thesis
Presented in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Graduate School of The Ohio State University
By
Claire Paisley-Jones, B.A.
Environmental Science Graduate Program
The Ohio State University
2011
Thesis Committee:
Casey Hoy, Advisor
Deborah Stinner
Jay Martin
Copyright by
Claire Paisley-Jones
2011
iv
Abstract
Many scientists have expressed a need for a new scalable approach to monitoring biodiversity that takes advantage of recent advances in technology, which could allow us to quickly identify and respond to ecological changes. Monitoring biodiversity through the use of remotely recorded sound is one method that has been proposed to fulfill these requirements.
The objective of this study is to develop an index (the Modified Acoustic Entropy
Index (H’)), using remotely recorded sound, to examine variations in acoustic entropy in agricultural soundscapes, and to test the ability of this index to indicate relative biodiversity levels in these systems. To do this, the H’ index examines the level of niche utilization of the acoustic soundscape, which is hypothesized to relate to the number of species and individuals present in a location. The resulting H’ values can then be used to compare soundscape patterns and acoustic diversity levels over time and between locations.
Six locations with divergent Agricultural Health Index (AHI) levels, were selected to test the H’ index. Analyses were conducted to address five key questions: (1) Is there a significant relationship between the acoustic entropy (H’) of sites and their agroecosystem health index values, and/or the AHI’s component variables (Plant landscape diversity (BD), soil quality (SOIL), topography (ELV), social organization
v (SOC), farm economics (CAUV), and land economics (MKT)); (2) Are H’ levels comparable to those found using traditional measures of biodiversity (plant transect, insect pitfall, and avian call number); (3) How does the sampling radius of H’ compare to the sampling radius of traditional methods; (4) What is the length of sampling needed to ensure significant power of analysis; and (5) can the H’ index be used to identify and analyze anthropogenic disturbances.
The results of this study indicate that (1) during certain time periods the H’ index is significantly related to the Agricultural Health Index, plant heterogeneity, and soil quality; (2) In contrast, the standard methods were not related to the biodiversity measure, Plant heterogeneity. Furthermore, none of the traditional methods tested were correlated with each other, calling into question their usefulness as a surrogate for total diversity; (3) Additionally, the sampling radius of the H’ index (> 1,000 m) was found to be greater than that of the traditional methods; (4) For six sites, a sampling period of greater than seven weeks is recommended. Because the index is calculated over a longer period of time, however, it can be used to discern diel and seasonal patterns of animal activity, which cannot be detected as easily using more traditional methods; (5) While the
H’ index could be used to identify anthropogenic acoustic disturbances, no difference in rebound time between sites was detected.
Therefore, although the H’ index was not correlated with any of the traditional methods, the consistency of the patterns seen and the agreement of the H’ with the AHI tentatively suggest that the H’ index may be used to detect significant differences in diversity as or more consistently than traditional methods of biodiversity assessment.
vi
Dedication
Dedicated to my amazing mother, Heather Paisley-Jones
vii
Acknowledgments
I would like to thank my advisor Dr. Casey Hoy, as well as my committee members Dr. Jay Martin and Dr. Deborah Stinner, for their guidance, support, and patience during the completion of this research and thesis. I would also like to thank Mic
Miller for his assistance in the development of the H’ index and the Matlab scripts used in this study. Thank you to Krishna Vadrevu for assisting me with the AHI data. Thank you also to the landowners of the farms used in this study.
This work would not have been possible without the support of the Environmental
Science Graduate Program, and grants from the National Science Foundation, and the
Ohio Agricultural Research and Development Center’s SEEDs Program.
Finally, I would like to thank my friends and family for their continued support and encouragement. To my Father, Lawley Paisley-Jones, for pushing me to go to graduate school. To my mother, Heather Paisley-Jones, for her tireless efforts to foster my love of nature and science over the past 25 years. This would never have been possible without you. To my stepmother, Sandy Burford, for editorial advice and tolerating my messy studying habits. Finally, to my friend Carli Edington, for her encouragement, and for accompanying me on many, many trips to the field.
viii
Vita
June 17th 1986 ...... Born in Washington, DC
2004 ...... International Baccalaureate Degree Washington-Lee High School,
2008 ...... B.A. Biology, College of Wooster
2008 to 2009 ...... National Science Foundation GK-12 Fellow, The Ohio State University
2009 to 2010 ...... Graduate Teaching Associate, The Ohio State University
Fields of Study
Major Field: Environmental Science
Area of Emphasis: Agroecosystems
ix
Table of Contents
Abstract ...... v Dedication ...... vii Acknowledgments ...... vii Vita ...... ix Table of Contents...... x List of Tables ...... xii List of Figures ...... xiii
Chapter 1: INTRODUCTION 1.1 Biodiversity ...... 1 1.2 Biodiversity in Agroecosystems ...... 4 1.2.1 Regulating Services...... 9 1.2.2 Supporting Services...... 16 1.2.3 Cultural Services ...... 22 1.3 Biodiversity measures ...... 24 1.4 Bioacoustics ...... 44 1.5 This study...... 49 1.6 Objectives and Thesis...... 52
Chapter 2: The Modified Acoustic Entropy Index as a Measure of Biodiversity 2.1 Introduction ...... 54 2.2 General Methods ...... 57 2.2.1 Study Area ...... 57 2.2.2 Sensor Platforms...... 59 2.3 Comparison of the Modified Acoustic Entropy (H’) Index and Traditional Measures of Biodiversity ...... 60 2.3.1 Calculation of the Modified Acoustic Entropy (H’) Index ...... 60 2.3.2 Calculation of Traditional Measures of Biodiversity ...... 64 2.3.3 Determination of AHI and Component Variable Values………….67 2.3.4 Sampling Radius of the Acoustic Sensor ...... 67 2.3.5 Data Analysis ...... 70 2.4 Results ...... 72 2.5 Discussion ...... 92
x Chapter 3: Temporal Parameters of the Modified Acoustic Entropy (H’) Index 3.1 Introduction ...... 101 3.2 Methods ...... 102 3.2.1 Statistical Power of the Modified Acoustic Entropy Index (as a function of sampling time?) (i.e. necessary length of sampling)...... 102 3.2.2 Diel and Seasonal patterns in the soundscape, and their relationship to biodiversity measurement ...... 104 3.2.2 Rebound Time of Soundscape After Extended Periods of Anthropogenic Acoustic Disturbance ...... 105 3.3 Results ...... 106 3.3.1 Power Analysis ...... 106 3.3.2 Diel and Seasonal Patterns ...... 110 3.3.3 Anthropogenic Disturbance ...... 123 3.4 Discussion ...... 124
Chapter 4: SYNTHESIS and CONCLUSIONS 4.1 Overview of Study ...... 137 4.2 Key findings ...... 139 4.3 Future Applications and Research ...... 142 4.4 Limitations and Future Research ...... 143
References ...... 147
Appendix A: Matlab Script for H’ Index (Miller 2008) ...... 157
Appendix B: Plant Diversity Data ...... 159
Appendix C: Insect diversity data...... 163
Appendix D: Regression data …………………………………………………………165
xi
List of Tables
Table 1: Ecosystem services (MA 2005)...... 7
Table 2: Types of bias in biodiversity data ...... 27
Table 3: Biodiversity measures and sampling methods, listed in order of decreasing directness, with citations of articles that discuss or use the method...... 28
Table 4: Summary of the key variables and data used to describe Agroecosystem Health. (Vadrevu et al. 2008...... 51
Table 5: Method of biodiversity assessment with actual indicator measured and relevant scale of measurement...... 57
xii
List of Figures
Figure 1. Study area. White dots indicate High AHI sites and black dots indicate Low AHI sites. Each site is labeled with a site ID. Numbers below identify replicate identification number. Replicate sites were chosen from the circular areas chosen for further study in the AHI study. These pairs are referred to as “circles” in the analysis below...... 59
Figure 2: Wave form and spectrogram of the test sequence tones. Each band represents a tone, each group of tones represents a 10 dB step. Color in the spectrogram represents the amplitude of the tone...... 68
Figure 3: Average daily temperature for the months during which testing took place in 2009 (OARDC Weather Systems 2011)...... 71
Figure 4. Comparison of the Acoustic Entropy Index from 2009 and 2010 combined and the component variables of the AHI index, as well as the AHI, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01) ...... 73
Figure 5. Comparison of the Acoustic Entropy Index from 2010 and the component variables of the AHI index, as well as the AHI, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01)...... 74
Figure 6. Comparison of the Acoustic Entropy Index from 2009 and the component variables of the AHI index, as well as the AHI, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01)...... 75
Figure 7. Comparison of average half-hour Acoustic Entropy (H’) values for test sites in 2009...... 76
Figure 8. Comparison of average half-hour Acoustic Entropy (H’) values for test sites in 2010...... 77
Figure 12. Comparison of average half-hour Acoustic Entropy (H’) values for test sites in 2009 and 2010 combined...... 78 xiii Figure 10. Comparison of the Spectral Entropy Index from 2009 and 2010 and the component variables of the AHI index, as well as the AHI, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01)...... 79
Figure 11. Comparison of the Temporal Entropy Index from 2009 and 2010 and the component variables of the AHI index, as well as the AHI, at four scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01)………………………………………………………………………………80
Figure 12. Comparison of the Acoustic Entropy Index and Shannon’s diversity index calculated for: a) plant transect (r = 0.316, n = 6, p = 0.542), b) ground dwelling insects (r = -0.142, n = 6, p = 0.789), and c) number of bird calls (r = 0.147, n = 6, p = 0.782…...82
Figure 13. Comparison of: a) the plant transect Shannon’s diversity index and the insect pitfall trap Shannon’s diversity index values (r = 0.264, n = 6, p = 0.613). b) the insect pitfall trap Shannon’s diversity index values and the number of bird calls (r = -0.517, n = 6, p = 0.0.293), and c) the plant transect Shannon’s diversity index the number of bird calls (r = -0.743, n = 6, p = 0.090)...... 83
Figure 14. Comparison of Plant Transect and the component variables of the AHI index, as well as the AHI index, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01). (See appendix for regression tables)...... 85
Figure 15. Comparison of Insect Pitfall and the component variables of the AHI index, as well as the AHI index, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01). (See appendix for regression tables)...... 86
Figure 16. Comparison of avian call number and the component variables of the AHI index, as well as the AHI index, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01). (See appendix for regression tables)...... 87
Figure 17. Sampling radius of the SongMeter (SM1) as determined using multiple frequency tones played at a six constant amplitudes, at increasing distances from the sensor. Linear regression fit lines are used to estimate the distance beyond which tones are not detected: a) 10,000 Hz tone, b) 5,000 Hz tone, c) 2,000 Hz tone, d) 1,000 Hz tone, e) white noise...... 89
Figure 18. Observed sampling radius of the SongMeter (SM1) as determined using multiple frequency tones played at a six constant amplitudes, at increasing distances from the sensor, showing excess attenuation. Logarithmic lines display theoretical attenuation: xiv a) 10,000 Hz tone, b) 5,000 Hz tone, c) 2,000 Hz tone, d) 1,000 Hz tone, e) white noise ...... 91
Figure 19. Relationship of the number of weeks of sampling included in mean H’ in 2009 to the component variables of the AHI index, as well as the AHI, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI…………………………………………………………………………………..108
Figure 20. Relationship of the number of weeks of sampling included in mean H’ in 2010 to the component variables of the AHI index, as well as the AHI, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AH…………………………………………………………………………………....109
Figure 21. Mean hourly H’ values for 2009. Letters indicate significant relationships (A = BD0: slope = 0.015, SE 0.005, t = 2.902, p = 0.44; B = BD3: slope = 0.016, SE 0.006, t = 2.786, p = 0.50) ……………………………………………………………………111
Figure 22. Mean hourly H’ values for 2010. …………………………………..………112
Figure 23. Mean hourly H’ values for 2009 and 2010 combined………………………113
Figure 24. Average weekly H’ in 2009 values with SD error bars. Significant differences indicated with letters (A = SOIL, B = SOIL 3). ………………………………………114
Figure 25. Average two week H’ in 2009 values with SD error bars. Significant differences indicated with letters (A = SOIL, B = SOIL 3)………………...... 115
Figure 26. Average three week H’ in 2009 values with SD error bars. Significant differences indicated with letters (A = SOIL, B = SOIL 3)……………… …………...116
Figure 27 Average four week H’ in 2009 values with SD error bars. Significant differences indicated with letters (B = SOIL 3)……………………………………… 117
Figure 28. Average four week H’ values for 2010 with SD error bars. Significant differences indicated with letters (A = AHI0, B = AHI3, C = BD0, D = BD3, E = SOIL0). ………………………………………………………………………………119
Figure 29. Average two week H’ values for 2010 with SD error bars. Significant differences indicated with letters (B = AHI3, C = BD0, E = SOIL0)………………..120
xv Figure 30. Average three week H’ values for 2010 with SD error bars. Significant differences indicated with letters (A = AHI0, B = AHI3, D = BD3)…………………121
Figure 31. Average four week H’ values with SD error bars. Significant differences indicated with letters (A = AHI0, B = AHI3, D = BD3, E = SOIL3)…………………122
Figure 32. Difference between the average mean H’ and the average observed H’ during each period for each site. Significant differences indicated by stars. OH21 (df = 2, F = 5.075, p = 0.016), OH20 (df = 2, F = 9.066, p = 0.004), OH25 (df = 2, F = 13.987, p < 0.001), OH24 (df = 2, F = 2.005, p = 0.177), OH22 (df = 2, F = 6.000, p = 0.579), OH23 (df = 2, F = 6.557, p = 0.004)…………………………………………………………124
xvi
Chapter 1: Introduction
1.1 Biodiversity
Biodiversity loss is occurring globally at a staggering rate (Ulgiati and Brown
1998; United Nations 1992). Difficulty assessing biodiversity levels in a timely fashion, however, makes it challenging for scientists to determine where and to what extent these losses are occurring. The development of accurate, simple, and responsive methods of biodiversity assessment, to determine the number and variety of species in an area, and to evaluate the difference between communities in different locations, or over time, is therefore of paramount importance to the efforts of conservation biology (Ulgiati and
Brown 1998; United Nations 1992).
The task of assessment, however, is complicated by the debate surrounding the definition and importance of biodiversity. The current urgency with which much of the public and scientific community view biodiversity can largely be traced to the United
Nations Conference on the Environment and Development’s Rio Convention of 1992
(Duelli and Obrist 2003; Moonen and Barberi 2008), which defines biodiversity as “the variability among living organisms from all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems” (United
1 Nations 1992). With such a broad definition, however, the meaning of biodiversity, the level at which it should be assessed, and its importance to society is often unclear.
Further complicating the situation, many studies use the term biodiversity without specifying which type or level of biodiversity they are referring to, thus increasing the confusion surrounding the term (Moonen and Barberi 2008). Because of the range of definitions of biodiversity, it is crucial that studies clearly state what is meant by the terms used in order to clearly define the scope and utility of their work (Duelli and Obrist
2003).
Before the Rio Convention, biodiversity was generally viewed in terms of environmental health or impact on environmental services (Duelli and Obrist 2003). This focus on the instrumental value of biodiversity to man gave a clear mandate to the need for assessment, and suggested levels at which biodiversity should be examined to answer particular questions. Because the Convention’s definition does not explicitly mention the benefits of biodiversity, however, after Rio, many in the public and scientific community increasingly began to think of biodiversity solely in terms of intrinsic value (Duelli and
Obrist 2003; Moonen and Barberi 2008). While most can agree that biodiversity is intrinsically valuable, this focus often does not present clear direction for area or level of assessment. Today, ecosystem services are again coming to the forefront of scientific and public discussion, and the term biodiversity most often implies some combination of intrinsic and instrumental attributes (Duelli and Obrist 2003; Moonen and Barberi 2008).
Because biodiversity is both intrinsically valuable and linked to important ecosystem services, the preservation of biodiversity is critical. Human actions, however, increasingly threaten this valuable resource (Dobson 2005). Anthropogenic disturbance
2 factors include: overexploitation, habitat destruction, disturbance of hydrological regimes, pollution, climate change, habitat fragmentation, introduction of invasive species, release of toxins, biological simplification, and genetic pollution (Altieri 1999;
Kennedy et al. 2002; McLaughlin and Mineau 1995; Moonen and Barberi 2008;
Rodriguez and Wiegand 2009; Swift, Izac, van Noordwijk 2004; Tilman et al. 2001). The generation of such pressures is largely the result of overarching socioeconomic processes that became common during the Green Revolution in the mid-twentieth century (Altieri
1999; Rodriguez and Wiegand 2009; Spangenberg 2007; Tilman et al. 2001), before the effect that such practices could have on the surrounding environment was known, and before the link between biodiversity and ecosystem services was widely recognized by much of Western society (Rodriguez and Wiegand 2009). In order to mitigate the negative effects of human actions on biodiversity, a robust, responsive, and reliable measure must be developed that will give scientists and the public an accurate picture of how our actions affect biodiversity, and how biodiversity levels affect us (Dobson 2005;
Duelli 1997; Kremen and Ostfeld 2005; Spangenberg 2007).
Focusing on biodiversity in agricultural systems, this chapter describes the role of biodiversity in agroecosystems (section 1.2), the current state and history of biodiversity assessment (section 1.3), and the use of bioacoustics as an indicator of biodiversity
(section 1.4). My study, which uses remotely recorded sound to measure biodiversity in agricultural landscapes is then briefly outlined (sections 1.5 and 1.6), and will be explored in detail in the following chapters.
3
1.2 Biodiversity in Agroecosystems
Biodiversity in an agricultural context is of particular interest because agroecosystems represent a unique intersection between human society and the environment. Within agricultural systems, biodiversity is critical for the maintenance of primary production, soil formation, nutrient cycling, biological control of pests, and many other factors on which food production is dependent, and must therefore be monitored and preserved (Altieri 1999; Altieri and Rosset 1996; Swift, Izac, van
Noordwijk 2004). The Conference of Parties (COP), established by the UN Convention on Biodiversity, has recognized that “the special nature of agricultural biodiversity, its distinctive features, and problems need distinctive solutions” (United Nations 1992).
Biodiversity in agroecosystems, however, has not received the attention it deserves from many farmers and much of the public (Alkorta, Albizu, Garbisu 2003; Dudley et al.
2005). This neglect owes much to the persistent idea that humans, and human dominated ecosystems are outside of “Nature”, and therefore do not warrant ecological preservation.
(Because our language’s definition of nature is also multi-faceted, in this paper, “Nature” will denote this normative definition of pristine areas with little to no human intervention, while the term natural will denote unmanaged habitats, without any intended normative connotation, and may be used interchangeably with the term unmanaged.)
To effectively address biodiversity in an agricultural context, it is important to recognize the difference in the role that biodiversity plays in agroecosystems compared with natural ecosystems. While natural biodiversity is the source of all agricultural plants and animals, the original association of biodiversity to agriculture was in the selection of
4 more productive and palatable species, and the reduction of unproductive species, and can therefore be characterized primarily as a reduction in diversity within the managed area (although this is not necessarily true when the boundaries of the system are expanded to include the marginal lands surrounding the cultivated area or the community of soil organisms (Swift, Izac, van Noordwijk 2004)) (Altieri 1999; Hagvar 1994; United
Nations 1992). Furthermore, while community composition is determined by complex patterns of natural selection in unmanaged habitats, community composition in agroecosystems is highly influenced by human management practices, such as the cultivation of certain species and practices aimed at the regulation of particular groups of pest organisms, defined as species that negatively affect production (Duelli 1997;
Moonen and Barberi 2008; Swift, Izac, van Noordwijk 2004). For these reasons, the approach to diversity in agroecosystems is logically different than that used in natural ecosystems (Moonen and Barberi 2008; United Nations 1992). Because of our unique role in this relationship, man has the ability to positively influence biodiversity in agroecosystems in ways that are not possible in natural or other human controlled ecosystems.
Despite these substantial differences, agriculture and the natural biodiversity of the surrounding ecosystem are still strongly interdependent (de Groot, Wilson, Boumans
2002; United Nations 1992). Agroecosystems consist of three interacting subsystems: the productive system of managed fields, the natural or semi-natural habitats surrounding them, and the human settlements and infrastructure with which they are associated
(Moonen and Barberi 2008). Biodiversity preservation in the productive system is most often associated with the maintenance of ecosystem services, or “benefits people obtain
5 from ecosystems” including goods, such as food and fiber, and services, such as nutrient cycling and disease regulation (Altieri 1994, (de Groot, Wilson, Boumans 2002; MA
2005). The rationale for preservation in the natural system, on the other hand, is most often aesthetic or intrinsic (Altieri 1994, (de Groot, Wilson, Boumans 2002). The relationship between these adjacent systems is often viewed as antagonistic, with productive systems negatively affecting biodiversity in the natural system, and the natural system as a threat to the productive system as a source of pest species. The mutual dependence of these systems is, again, often disregarded because of the persistent view that people and their activities are separate from “Nature” (Moonen and Barberi 2008;
Naveh 1994). The continued use of unsustainable farming practices and subsequent loss of biodiversity is at least partially due to this misconception.
To better understand the interdependence of these systems, and the relationship of biodiversity to ecosystem services, ecosystem services can be classified as belonging to one (or several) of four functional categories: provisioning, regulating, cultural, and supporting services (MA 2005).
6 Table 1. Ecosystem services (MA 2005)
Provisioning Services Regulating Services Cultural Services Products obtained from Benefits obtained from regulation Nonmaterial benefits obtained ecosystems of ecosystem processes from ecosystems
• Food • Climate regulation • Spiritual and religious • Fresh water • Disease regulation • Recreational and ecotourism • Fuel wood • Water regulation • Aesthetic • Fiber • Water purification • Inspirational • Biochemicals • Pollination • Educational • Genetic resources • Sense of place • Cultural heritage
Supporting Services Services necessary for the production of all other ecosystem services
• Soil Formation • Nutrient cycling • Primary production
While agriculture can be a significant contributor of sustainable uses of biodiversity, it is also a major driver of biodiversity loss, due to the large land area and the great amounts of physical and chemical inputs typically used to maintain high agricultural yields (Flynn et al. 2009; McLaughlin and Mineau 1995; Rodriguez and
Wiegand 2009; Tilman et al. 2001; United Nations 1992). Many studies on biodiversity in agriculture have shown that both species richness (the number of species in a given area) and functional diversity (the variety and distribution of biological traits in an area) of birds, mammals, insects, and plants decrease with increased agricultural intensity
(Flynn et al. 2009; Kremen and Ostfeld 2005). In a review of 20 studies, Flynn et al.
(2009) found that agricultural and semi-natural landscapes were more than twice as likely than natural habitats to have lower functional diversity of mammal and bird communities, suggesting that species loss in these landscapes may be nonrandom, with functionally unique species lost at a greater rate than functionally redundant species. Because 7 agroecosystems are generally species poor, and thus have low functional redundancy, the addition or loss of species is likely to noticeably alter ecosystem functioning to a greater degree than in other ecosystems (Moonen and Barberi 2008). For instance, the negative effect of invasive weed species is often pronounced in agroecosystems because they have little competition (Kennedy at el. 2002), while the addition of a single species (such as a pollinator) can greatly improve ecosystem function and yield (Kremen and Ostfeld 2005).
Furthermore, agriculture tends to be most intense on specific landscape types, and in certain regions, resulting in disproportionate pressure being put on particular habitat types such as plains, wetlands, and forests (McLaughlin and Mineau 1995).
Typically, modern agricultural systems are maintained by substituting or augmenting natural ecosystem services with human labor and/or chemical additives
(Swift, Izac, van Noordwijk 2004). These substitutions have largely been adopted as a means to mediate the risk associated with dependence on natural ecosystem services, and to replace services that have been degraded or lost to simplification of the system. While such practices can make some functions more predictable and reliable, they often have cascading effects that degrade other natural ecosystem services in unpredictable ways, such as nutrient depletion, erosion, and greater susceptibility to pests (Swift, Izac, van
Noordwijk 2004). These lost services must then be replaced with artificial methods.
Decreasing management intensity and encouraging biodiversity in agroecosystems and surrounding landscapes can restore many valuable ecosystem services. In the paragraphs below, I will discuss some of the beneficial relationships between biodiversity levels and the functioning of regulating and supporting services as they relate to provisioning ecosystem services or crop yield. Cultural ecosystems services and their relationship to
8 biodiversity will be discussed at the end of the section. This is by no means an exhaustive list, but rather illustrates the costs and benefits of different agricultural practices.
1.2.1 Regulating Services
Biological Control of pests
One way that associated biodiversity can positively influence crop production is through natural biological control of pest populations. The simplification of many agricultural systems, however, has led to the loss of many ecosystem self-regulation abilities, which has often led to the establishment of problematic pest populations, which require large amounts of time, money, and chemical application to control (Cardinale et al. 2003; Crowder et al. 2010; Fournier and Loreau 1999; Gurr, Wratten, Luna 2003;
Holland et al. 2005; Nicholls, Parrella, Altieri 2001; Tilman, Reich, Knops 2006). Such application of chemical pesticides themselves causes a great threat to global biodiversity.
Some of the more persistent and volatile pesticides have the ability to spread through air and water cycles. Others can bioaccumulate in food chains and thus have far reaching effects on the health of humans and other organisms at times and distances far beyond the initial application site (Tilman et al. 2001). At the current rate, global pesticide production is expected to increase 2.7 times by 2050 (although some projections are as high as 4. 8 fold) (Tilman et al. 2001). Even with such intense levels of inorganic management, pest resource competition and crop damage are common and costly occurrences in agriculture.
A wide body of research, however, suggests that reintroduction of biodiversity in agroecosystems can restore the natural community dynamics that keep pest species
9 populations in check (Altieri 1999; Altieri and Rosset 1996; Cardinale et al. 2003;
Crowder et al. 2010; Fournier and Loreau 1999; Gurr, Wratten, Luna 2003; Holland et al.
2005; Kennedy et al. 2002; Letourneau and Bothwell 2008; Nicholls, Parrella, Altieri
2001; Tilman, Reich, Knops 2006). Such effects can be seen in the active suppression of many crop pests by increased abundance and diversity levels of beneficial predator and parasitoid species (Cardinale et al. 2003; Crowder et al. 2010; Fournier and Loreau 1999;
Gurr, Wratten, Luna 2003; Holland et al. 2005; Kennedy et al. 2002; Letourneau and
Bothwell 2008; Nicholls, Parrella, Altieri 2001). Furthermore, studies which have experimentally manipulated predator and pathogen levels, indicate that increased diversity (Cardinale et al. 2003) and evenness (Crowder et al. 2010) of antagonists populations can have positive synergistic effects, and thus decrease pest densities and increased plant yields at higher levels than would be expected from abundance alone.
The strength of these services is dependent upon the presence and quality of field bordering (or marginal) habitats. A large body of research has shown that diverse levels of adjacent vegetation, and the presence of certain weedy species in particular, can significantly decrease crop damage, by providing habitat with easy field access to beneficial natural predators, and by providing an alternate food source to herbivorous pests (Altieri 1999; Duelli 1997; Fournier and Loreau 1999; Holland et al. 2005;
McLaughlin and Mineau 1995; Moonen and Barberi 2008; Nicholls, Parrella, Altieri
2001; Rodriguez and Wiegand 2009). In many agroecosystems, however, increasing field sizes have progressively led to the loss of these marginal habitats, which have high structural heterogeneity, which serve as important barriers to disease and pest distribution, and are crucial to agricultural and larger scale biodiversity (Altieri 1999;
10 Duelli 1997; Fournier and Loreau 1999; Gurr, Wratten, Luna 2003; Holland et al. 2005;
McLaughlin and Mineau 1995; Moonen and Barberi 2008; Nicholls, Parrella, Altieri
2001; Rodriguez and Wiegand 2009). While this increase in field size was mainly adopted to increase machinery efficiency, a study by Rodriguez and Wiegand (2009) indicates that beyond a certain threshold size, efficiency does not increase. The findings of this study suggest that marginal strips above 2 ha can be restored in most fields without significantly decreasing machinery efficiency.
The restoration of these marginal lands could subsequently aid in the restoration of bio control services, by providing valuable habitats for beneficial non-agricultural species, provide alternate food sources for pests, and restore crucial disease barriers
(Altieri 1999; Dudley et al. 2005; Rodriguez and Wiegand 2009), Fournier and Loreau
2001, (Gurr, Wratten, Luna 2003; Nicholls, Parrella, Altieri 2001). Indeed, A 2001 study by Fournier and Loreau found that the creation such hedges adjacent to cropped areas increased species diversity of carabid beetles at both local and landscape levels, and supported populations with diversity similar to those in small remnants of ancestral forest
(Fournier and Loreau 2001). Additionally, marginal habitats can serve to connect larger areas of preserved land, thus greatly increasing the efficacy of such preservation techniques, and their beneficial effects on agricultural systems (Altieri 1999; Dudley et al. 2005; Rodriguez and Wiegand 2009), Fournier and Loreau 2001, Nicholls et al. 2001).
The influence of such restored habitats is, however, limited by their number and the distance that natural enemies must travel to disperse into the cultivated area (Nicholls et al. 2001).
11 In addition to marginal habitats, increasing plant diversity within the cultivated field can improve biocontrol services. This effect can be seen in studies of annual polycultures, including orchards with ground cover vegetation (Altieri 1999; Altieri and
Rosset 1996), which due to their more consistent availability of food and microhabitats, have been shown to sustain higher natural enemy populations, and thus lower herbivore loads and related crop damage than comparable monocultures (Altieri 1999; Altieri and
Rosset 1996; Gurr, Wratten, Luna 2003). Similarly, creating corridors of non-crop species within cultivated fields can increase biocontrol by providing an alternate food source for predator species and thus avoiding the typical pattern delayed colonization cause by coupling of pest and predator populations and allows direct access to interior parts of the field, and thus expands the positive affects of natural predators, which are typically seen mainly only in field margins (Nicholls et al. 2001, (Cardinale et al. 2003;
Gurr, Wratten, Luna 2003). A variation of this practice, know as “beetle banking”, where low ridges are planted with perennial grasses within the crop area, has been practiced with great success in Europe for over a decade (Gurr, Wratten, Luna 2003).
When changes to crop mixture or the planted area are not possible, changes within the field itself can increase biocontrol services from natural predators. Withholding herbicide from the headland (where soil compaction from turning farm machinery generally causes low productivity of crops) can encourage the growth and diversity of beneficial plant species, which can harbor natural predators and serve as an alternative food source of pests (Gurr, Wratten, Luna 2003). Alternatively, spatially and temporally decoupling farm operations that can deplete natural predator populations, such as soil cultivation and pesticide application, can serve to decrease the effects of these
12 disturbances on predator populations (Gurr, Wratten, Luna 2003; Holland et al. 2005).
For instance, strip-cutting, or staggering the harvest of parts of a crop, acts to preserve beneficial predator habitats within the field (Gurr, Wratten, Luna 2003).
Pollination Services
Another potentially beneficial interaction of agriculture and natural biodiversity is pollination services. Animal mediated pollination services are particularly important to agriculture, as 75 percent of the top cultivated (and 84 percent of all cultivated food crops) plant species are at least partially pollinated by animals (Gruenewald 2010; Klein et al. 2007). For many crops, bees provide this service. A multitude of factors, however, currently threaten managed and wild bee populations around the world. These include: habitat loss and fragmentation, damage from pathogens and parasites, pesticide toxicity, climate change and cascading effects from invasive plant and animal species (Allen-
Wardell et al. 1998; Grixti et al. 2009; Gruenewald 2010; Kevan 1999). The health of pollinator populations of bees is so closely tied to the health and productivity of many ecosystems, that their diversity has been suggested as an environmental indicator (Kevan
1999).
Because of the declines mentioned above and the loss of natural diversity in areas surrounding agroecosystems, many farmers have come to rely on rented colonies of honeybees (Apis mellifera) to pollinate their crops (Gruenewald 2010; Kremen and
Ostfeld 2005). However, this system of substitution of natural pollinator services with a single imported species may place crops in unnecessary jeopardy. Decreases in the number of managed honeybee colonies, caused by loss of interest in bee keeping as a
13 hobby, increasing cost and complication of bee keeping, and large die-offs caused by parasitic mite infection have led to managed pollinator shortages throughout the country
(Gruenewald 2010; Kremen and Ostfeld 2005; Shuler, Roulston, Farris 2005). Studies suggest, however, that unmanaged wild bee communities can provide adequate pollination services alone, or enhance the services provided by imported honeybee colonies (Kremen and Ostfeld 2005). The presence of such wild species can thus protect farmers from shortages of honeybees, or could entirely replace the services of rented colonies (and thus decrease the cost of crop production) (Kremen and Ostfeld 2005).
The strength of these native pollination services, however, is closely tied to the species richness and abundance of wild bees, both of which generally decline rapidly with increased agricultural intensification (Kremen and Ostfeld 2005). The results of an increasing number of studies indicate that this effect may be due to the loss of plant diversity in areas surrounding agricultural field, which have traditionally been a source of food and shelter to wild bee populations (Kim, Williams, Kremen 2006; Winfree and
Kremen 2009). Furthermore, while native bee pollinators may all generally decline in response to the loss of native vegetation, they do not appear to decline at equal rates.
Generally, the species of bees that are the most efficient pollinators are also the most sensitive to intensification (Kremen and Ostfeld 2005). For instance, the bumblebee
(Bombus spp.) is one of the most effective pollinators of many crop plants, and also one of the species most negatively effected by the loss of native vegetation (Winfree and
Kremen 2009). This is due, at least in part, to the limited flight range, long colony cycle, and specific habitat and foraging requirements of bumblebees (Grixti et al. 2009). A study of museum collection data on bumblebees in Illinois found that over half of the
14 bumblebee species historically found in the state are now either locally extinct or have greatly declined in distribution (Grixti et al. 2009). These results are similar to those found in Canadian and European studies, indicating a global pattern of decline (Grixti et al. 2009). Furthermore, Grixti et al. (2008) found that the majority of this decline occurred from 1940 to 1960, and thus coincides with the period of agricultural intensification, suggesting that the decline of the bumblebee may be linked to intensification practices including: increases in field size, destruction of marginal lands, increased insecticide use, and the conversion of wildflower containing pastures to corn soy rotations (Grixti et al. 2009).
In addition to the indirect effects (such as habitat loss) of agriculture on wild bee populations, many agricultural practices, such as tillage of soils (Shuler, Roulston, Farris
2005) and pesticide application (Brittain et al. 2010) have been found to directly decrease bee populations (Kim, Williams, Kremen 2006). The effects of soil disturbance on wild bee populations can be seen in a 2005 on the effects of tillage on populations of bees in pumpkin and squash fields, which found that tillage decreased population levels of ground nesting bees (which were the most effective pollinators of the crops studied) but did not effect population levels of the less effective pollinators that did not nest in the field (Shuler, Roulston, Farris 2005). While the exact mechanism of decline was unclear, the authors suggest that these declines were likely caused by decreases in the larval population of the ground nesting bees, caused either by direct injury or by the collapse of tunnels leading to the larval nest during tilling (Shuler, Roulston, Farris 2005). Similarly, the effects of pesticide use on wild bee populations can be seen in a study of the effects of pesticide (fenitrothion) field application on pollinator populations in Italy, which found
15 effects at multiple levels. At the field level, species richness of wild bees was found to increase with multiple (but not single) applications of pesticide, suggesting cumulative toxic effects. At the landscape level, wild bee species richness declined in fields where pesticides were applied, but not in untreated fields. Finally, at the regional scale, lower bee species richness was observed in the area with higher pesticide application rates, than the adjacent area, which was less intensely managed (Brittain et al. 2010).
To mitigate these effects, specific habitat management steps, not unlike those used to combat pest invasion, can be taken to ensure the availability of appropriate habitat and key floral resources in the surrounding landscape, which are needed to support health wild bee populations (Allen-Wardell et al. 1998; Kevan 1999; Kremen and
Ostfeld 2005). Additionally, the inclusion of clover and other flowering plants into crop rotations, or allowing ruderal weed species to coexist with pollinator-dependent crops
(Carvalheiro et al. 2011), can help to sustain bee populations by providing an important additional source of food (Grixti et al. 2009). A measure of equal importance may be the education of farmers regarding the possible benefits of native bees and how their farming practices can affect their population levels, as many farmers are unaware that species other than honey bees and bumblebees can serve as effective pollinators (Shuler,
Roulston, Farris 2005).
1.2.2 Supporting Services
Supporting services are unique in that they are necessary for the production of the other categories of ecosystem services. Additionally, supporting services generally consist of slowly acting processes, and thus their effects on humans are often indirect or
16 take place over a much longer time period than other services (MA 2005). Because of the indirect and slow nature of these processes, agricultural practices have tended to use these services at a much greater rate than they can be naturally replenished. By focusing on maintaining and increasing biodiversity levels, however, it is possible to protect and strengthen supporting services.
Soil Formation
Natural soil formation is a lengthy process whereby rock is slowly disintegrated and through the addition of organic matter and the release of minerals, becomes fertile
(de Groot, Wilson, Boumans 2002). The resulting substance is a combination of organic components, called humus (which is composed of plant debris in various stages of decomposition, root exudates, and decomposed plant and animal material), and inorganic mineral components (Goulding, Jarvis, Whitmore 2008). Soil forms slowly over centuries, and can take centuries more to regenerate after erosion (de Groot, Wilson,
Boumans 2002). Furthermore, because dispersal rates for soil organisms are quite low, disturbed soils that have experienced a decrease in biodiversity do not recover quickly
(Brussaard, de Ruiter, Brown 2007). Once established, fertile soil provides a nutrient rich medium for plant growth and a habitat for a wide range of organisms.
As a dynamic system, the structure and components of soil can vary greatly spatially and temporally (Goulding, Jarvis, Whitmore 2008) and is influenced by both above and belowground biodiversity (Wardle et al. 2004). Maintenance of the soil structure is mediated by diverse communities of microorganisms that decompose organic materials and return their nutrients to the humus mixture, as well as soil macrofauna
17 (known as bioturbators) which create channels that influence the movement of gasses and water in soil, and plant root systems and vegetation cover that can help to retain soil by preventing erosion from wind and water (Brussaard et al. 1997; de Groot, Wilson,
Boumans 2002). A 2002 study found a positive relationship between soil aggregate stability and microbial biomass, as well as aggregate stability and earthworm biomass in organic systems, which showed significantly higher levels of mycorrhizae colonization and earthworm biomass and abundance, when compared to conventionally farmed systems (Mader et al. 2002). Additionally, this study found that the more diverse soil micro-organism community of the organic system had a lower metabolic quotient, and was thus able to more efficiently use resources for growth rather than merely for maintenance, contributing to a larger microbial biomass (Mader et al. 2002). These complex food web interactions among soil organisms, including the root systems of deep rooting plants that can act as transport systems for nutrients between the subsoil and topsoil, have a great effect on the overall production success of crop plants, by suppressing disease outbreaks through intense competition for resources, allowing the beneficial movement of water to roots, and by manufacturing and supplying valuable nutrients (Brussaard, de Ruiter, Brown 2007; Goulding, Jarvis, Whitmore 2008).
Nutrient Cycling
Related to the physical composition of soil are soil nutrients. Soil nutrient cycling, mediated by a complex food web of soil biota (including plant roots), is necessary for the maintenance of primary production (Brussaard, de Ruiter, Brown 2007). The availability of nutrients such as nitrogen (N), sulfur (S), and phosphorous, as well as macronutrients
18 like potassium, magnesium, calcium, sodium and chlorine in soil solution, are often limiting factors for crop production. For successful crop production, these nutrients must be continually cycled in the system (Brussard et al. 1997, (de Groot, Wilson, Boumans
2002; Hagvar 1998; Zak et al. 2003). In unmanaged systems, nutrient cycles are replenished and maintained through the decomposition of plant and animal material, as well as the return of nutrients in animal waste (Goulding, Jarvis, Whitmore 2008). In agricultural systems, however, because plant material and the nutrients it contains are removed from the field, and the resulting animal waste streams are not returned, these nutrients need to be artificially restocked in order to maintain a nutrient balance
(Goulding, Jarvis, Whitmore 2008; MA 2005; Swift, Izac, van Noordwijk 2004).
While the addition of nutrients to fields is necessary to maintain the fertility of the soil, application of fertilizers at levels beyond that which can be used or retained can have deleterious effects that reach far beyond the field. Humans already release as much N and P from fertilizers and untreated animal waste into ecosystems as all natural sources combined (Tilman et al. 2001). These additions have already drastically altered many terrestrial and aquatic ecosystems around the world. Increased levels of P cause eutrophication of surface water, especially freshwater bodies and streams (Goulding,
Jarvis, Whitmore 2008; Tilman et al. 2001). Increased levels of N cause eutrophication of estuaries and costal waters, as well as pollution of groundwater, and increases in the greenhouse gasses NO2 and NOx, which in turn leads to smog and acidification of terrestrial soils and freshwater bodies far from the initial application site (Tilman et al.
2001). The acidification of terrestrial ecosystems, as well as eutrophication, toxic algae blooms, and hypoxic zones in freshwater and costal systems caused by these applications
19 have already been a major driver of biodiversity loss (Tilman et al. 2001). If global application rates continue at the current rate, P fertilization will increase to 2.4 times the current rate, and N fertilization will increase 2.7 times by 2050, to an annual application of 263 x 106 MT (compared to only 140 x 106 MT from all natural sources) (Tilman et al.
2001). In order to avoid these consequences, many farmers now use methods that maintain nutrient cycles without the addition of excess nutrients, by restoring more natural nutrient cycling systems.
While it is known that soil organism communities play a significant role in soil nutrient cycles (McGuire and Treseder 2010), Brussaard et al 1997, (Goulding, Jarvis,
Whitmore 2008; Hagvar 1998; Mader et al. 2002), the effect of community structure on these structures has not been adequately described (McGuire and Treseder 2010),
Brussaard et al 1997, (Hagvar 1998). This is at least partially due to the staggering challenge of locating and classifying immense diversity in soil organisms (Brusaard et al.
1997, (McGuire and Treseder 2010). Species densities in soil are the highest found anywhere in nature (Hagvar 1998). The majority of soil species, however, are undescribed, and the number of species that inhabit soil is unknown (Brussaard et al.
1997). A large number of soil species can only be accurately identified using DNA analysis (Brussaard et al. 1997; Nannipieri et al. 2003; Torsvik and Ovreas 2002).
Furthermore, for bacterial and archaea the definition of species is obscure, and can thus complicate biodiversity calculations (Brussaard et al. 1997; Torsvik and Ovreas 2002).
In addition to classification issues, experimental manipulation of soil organism communities is extremely difficult due to the complexity of soils and soil systems
(McGuire and Treseder 2010). However, the results of a growing number of studies
20 indicated that diversity of multiple soil organisms does play a role in nutrient cycling
(McGuire and Treseder 2010; Miki et al. 2010; van der Heijden et al. 1998; Wardle et al.
2004; Zak et al. 2003). These experimentally demonstrated effects include: increased levels of levels of respiration and decomposition with increased microbial species richness (plateauing at higher richness levels), suggesting that microbial community structure could play a key role in predicting decomposition (McGuire and Treseder
2010); increased buffering ability against changes in decomposability of plant litter and increased evenness of plant populations with increased microbial diversity (Miki et al.
2010); and increased diversity and yield of plant species with increased diversity of arbuscular mycorrhizal fungi (AMF) species (van der Ploeg et al. 2009).
In an agricultural setting, using extensive management techniques such as rotational planting, low or no-tillage, organic amendments, and the maintenance of natural surrounding can contribute to more diverse, dense and active soil organism communities (Brussaard, de Ruiter, Brown 2007). Along these lines, organically maintained systems with higher soil organism diversity have been found to require significantly lower inputs of N, P, and potassium than conventional systems (Mader et al.
2002). Furthermore, because such systems with high soil organism diversity use nutrients more efficiently, when fertilizer must be used these systems capture and use added nutrients and thus reduce nutrient contamination of water systems (Brussaard, de Ruiter,
Brown 2007).
21 1.2.3 Cultural Services
Cultural services, or non-tangible services that humans obtain from ecosystems, differ from other ecological services, in that their value is dependent on the presence of a human beneficiary (MA 2005). Cultural services can take many forms including: cultural diversity, heritage, sense of place, and social relationships, as well as spiritual and religious values, traditional and formal knowledge systems, educational opportunities, and aesthetic values (MA 2005). Because of their clear human connection, cultural services vary more among societies than other ecosystem services, and highly dependent on societal values. In many locations, traditional farming practices represent centuries of interaction between societies, cultivated and native species and the environment in which they are located (Altieri 2004). In many locations, the uniqueness of the resulting cultural practices and their connection to a society’s identity makes these agricultural systems intrinsically valuable to their society (Bergstrom 2009; Hagvar 1994).
Cultural services, however, may also be linked to more utilitarian values. Because they predate intensive farming practices and chemical fertilizers, traditional farming practices are generally quite sustainable. The traditional knowledge and specialized cultivars present in these systems may serve as important source of information and genetic material that could be integrated into more modern farming systems to reduce their environmental impact (Altieri 2004; Hagvar 1994). Additionally, the rise of recreational and tourism in bucolic traditionally farmed landscapes, or argitourism, now serves as an added revenue stream for many farming communities, and is a monetary representation of the value societies place on preserving their agricultural heritage
(Bergstrom 2009; Hagvar 1994; Johnston and Duke 2009).
22 Today, many of the incentives for overproduction spawned by the Green
Revolution are decreasing, and current public demand has expanded to include non- market goods described above, such as aesthetic landscapes, historic and cultural environments, consideration of natural resources, and preservation of biodiversity, in addition to agricultural products (Altieri 1999; Bergstrom 2009; Rodriguez and Wiegand
2009). Management that satisfies or seeks to satisfy these goals has often been called multi-functional agriculture (Stobbelaar et al. 2009; van der Ploeg et al. 2009).
Furthermore, many now believe that agroecosystems should be managed in ways that promote the development of mechanisms that allow the systems to more easily recover from disturbances and autonomously perform basic agroecosystem processes like maintaining soil productivity, crop protection and productivity, and pest regulation
(Altieri 1999; Altieri and Rosset 1996; Moonen and Barberi 2008). This trend can be seen in the increasing popularity of and willingness to pay higher prices for organic and sustainably grown foods. Although such recent efforts aimed at preserving the agricultural resource base may positively affect environmental quality, and thus associated wild biota, many of these practices remain untested, and in such cases there is no guarantee that biodiversity is in fact being preserved (McLaughlin and Mineau 1995).
The lack of verification of the effectiveness of these practices has greatly hindered the widespread acceptance of such measures as worthy procedures. It is therefore necessary to accurately determine how agricultural intensification and conservation practices affect ecosystem services. Furthermore, accurate measures of biodiversity that can be linked to ecosystem function need to be developed (Flynn et al. 2009; Kremen and Ostfeld 2005;
United Nations 1992).
23 As the world’s population increases, agroecosystems will take the place of more and more native habitats and will play an increasingly large role in future global biodiversity. Thus, how we address the need for increased food production will largely determine the future of our own species. It is therefore essential to understand the way in which human practices affect biodiversity in these systems and develop management systems that minimize negative impacts. Too often, however, the negative impact of human activities on biodiversity is detected too late or not at all. Furthermore, most proposed solutions suggest additive measures for protecting biodiversity, rather than addressing the driving pressures that cause biodiversity losses (Spangenberg 2007). A reliable system for detecting biodiversity loss, before it’s effects become severe, is needed to allow ecologists to treat the cause and not just the symptom of biodiversity loss.
1.3 Biodiversity measures
While most societies, governments, and scientists now agree that biodiversity is both intrinsically and instrumentally valuable, and thus worthy of preservation, how to best define, assess, and preserve biodiversity is still highly debated. To better understand this debate, it is helpful to understand the levels at which biodiversity can be assessed, the methods that have been used for assessment, and the benefits and limitations of these assessment practices.
Three commonly recognized elements of biodiversity can be measured: (1) compositional, or the diversity of species in an area, (2) structural, or the diversity of groups occupying different niches and (3) functional, or the diversity of functional groups
24 in an area (Noss 1990). Compositional diversity is the most commonly measured of the three, based on the assumption that structural and functional biodiversity are either the result or cause of compositional diversity and are therefore reflected by this measurement
(Duelli and Obrist 2003). There are two common levels on which compositional biodiversity is measured. The first, and most straight forward, !- diversity indicates species richness in a single area, where each species is equally weighted (Whittaker
1972). The second, "- diversity refers to the comparison of species between ecosystems, where species are weighted based on their frequency (Whittaker 1972). Both ! and " diversity can be measured on three relative scales: (1) an ecosystem’s contribution to larger scale diversity, (2) comparisons in space, and (3) comparisons in time (Duelli
1997). The inconsistent use of ! and " diversity scales as indicators of biodiversity has often led to a debate between of the importance of species richness verses conservation value of certain species (Duelli and Obrist 2003). Both scales, however, are important to establish a complete view of the biodiversity of any ecosystem.
The most accurate way to measure biodiversity is with all taxa biodiversity inventories, established with intensive ground surveys (Gillespie et al. 2008). Such inventories, however, often require prohibitive amounts of time, and skilled scientists with the ability to accurately identify a wide range of species. As a result, these surveys can be extremely costly, and produce biased or dissimilar data between studies based on slight differences in collection (Dobson 2005; Gillespie et al. 2008; Letourneau and
Bothwell 2008). Furthermore, the time, training, and expense required to conduct these studies makes it extremely difficult to repeat them frequently enough to discern changes
25 in biodiversity related to seasonal rhythms or disturbances (Dobson 2005; Porter et al.
2005).
One reason for the debate surrounding the value of biodiversity in any ecosystem has been the difficulty of accurately measuring or even estimating biodiversity. Due to the prohibitive nature of all-taxa biodiversity inventories, numerous biodiversity indexes have been created to extrapolate biodiversity measures from smaller data sets (Duelli
1997; Sueur et al. 2008). Biodiversity is often quantified using “surrogates and correlates” such as indicator and keystone species or groups. Although similar in function, surrogates and correlates are distinctly different. A correlate of biodiversity is a measurable unit whose relationship to total biodiversity is statistically tested and supported (Duelli 1997). The more commonly used surrogate, on the other hand, is an
“intuitive estimation of biodiversity based on theories, models, or concepts” (Duelli
1997). These proxies, however, do not always effectively represent biodiversity in scale, function, or impact (Jeanneret, Schupbach, Luka 2003). The challenge faced here is the balance between reliable, repeatable, and financially reasonable approaches that minimize bias (Dobson 2005; Duelli 1997; Duelli and Obrist 2003). No matter the method chosen, because the number of species found is positively correlated with sampling effort, it is crucial that the methods used are strictly standardized (Duelli 1997).
Specific types of bias are discussed in Table 2. In the past, biodiversity has been estimated in many ways, each of which has their own advantages and disadvantages, many of which are discussed in Table 3 below.
26
Table 2. Types of bias in biodiversity data
Type of Bias Description Technique for control Citation Statistical bias Bias resulting from the tendency of a Rigorous statistical (Stockwell and statistical measure (mean, variance, analysis can help to control Peterson 2002) abundance, etc), to converge on a statistical bias. value that is not equal to its actual value.
Inductive bias Bias resulting from the assumptions Thorough testing of (Stockwell and made during the inductive creation of models using the scientific Peterson 2002) a model. That is, assumptions of method and rigorous outputs based on inputs that have not scientific practices can been encountered. help to control inductive bias. Sampling bias Bias resulting from assumptions made (see below) (Stockwell and regarding an entire population based Peterson 2002) on data from a subset of the population, when that subset is not representative of the entire population.
Presence-only Bias resulting from the collection of The creation of pseudo- (Stockwell and bias only positive data (presence) and not absences where a species Peterson 2002) (sampling bias) negative data (absence). has not been recorded to give background points for contrast.
Abundance Bias resulting from uneven data Collect data from points (Stockwell and bias collection in areas that are not equally throughout the Peterson 2002) (sampling bias) representative of the entire population territory studied. (i.e. rarity and hotspots).
Correlation Bias resulting from the tendency to Identify variables that are (Stockwell and bias sample more frequently in certain correlated with the bias Peterson 2002) (sampling bias) geographic areas (i.e. roadsides etc). and be careful not to over- include them in the analysis. Taxonomic Bias resulting from more effort being Attempt to include as wide (Dobson 2005) bias put into studying certain species than a range of taxonomic others. This may result from the groups as possible in difficulty of studying certain groups studies. which are either difficult to identify, distinguish, or locate. This may also result from certain groups being more appealing to the public and scientists as targets of study and conservation. Weather bias Bias resulting from the tendency to Remote sensing and collect field data only during periods capture techniques can of good weather. collect data in all weather.
27 Table 3. Biodiversity measures and sampling methods, listed in order of decreasing directness, with citations of articles that discuss or use the method.
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Total species Every species Plot to Time taken to Very time Statistical, (see general inventory present within ecosystem complete the consuming and inductive, discussion of sampling area. (although survey, to costly. sampling. biodiversity Can be recorded as such large longer periods Requires highly measures) richness, evenness, scales are with repeated trained etc. highly measures specialists. impractical). (although frequently
28 repeating
these studies can be highly impractical). Diversity of Number higher Similar to Similar to More cost/time May slightly (Gaston and higher taxonomic groups total species total species efficient than underestimates Blackburn 1995) taxonomic (Genus, Family) inventories. inventories. total species species orders present as a predictor inventory, but richness when (Santi et al. 2010) of the diversity of still very high, and species, based on the intensive. Saves slightly correlation of the time generally overestimates diversity of these spent when low. higher groups to the inventorying a Can’t be diversity of species. few hyper measured diverse groups. remotely
Continued
28 Table 3 continued
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Key indicator Indicator taxa of Similar to Similar to Cost and time to Inductive bias (Noss 1990) species diversity, which total species total species complete may be large. generally indicate inventories. inventories. keystone Indicators need something about the Although Although evaluations, as to be level of total decreased decreased well as training thoroughly diversity in the sampling sampling necessary are studied, in system. requirements requirements decreased by order to ensure make larger make larger focusing on one that the
29 scales more scales more or a small theorized practical. practical. number of relationship of species. the indicator to the larger ecosystem valid.
Continued
29 Table 3 continued
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Keystone The status of Similar to Similar to Depending on Biases are (Paine 1966) Species populations of total species total species the ease of similar to that species that have a inventories. inventories. locating, of indicator (Favreau et al. proportionally large Although Although identifying, and species. 2006) effect on the decreased decreased counting the structure and/or sampling sampling species, function of their requirements requirements monitoring ecosystem. make larger make larger keystone species scales more scales more may be much 30 practical. practical. more cost
efficient than other methods. This method, however, may still be quite labor intensive. Additionally, because of these species’ role in their ecosystems, it is often easier to justify and fund such studies. Continued
30 Table 3 continued
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale H’ (Sueur The diversity sound Range or Period of Cost is limited Inductive is the (Sueur et al. 2008) signatures in the recording recording. to the greatest 2008) soundscape device. May be purchasing of concern, repeated with recording although reasonable equipment and statistical and ease to the deploying of sampling bias increase time sensors. are also scale. Little training is possible
31 needed to place and operate sensors and analysis program. The interpretation of data, however, takes slightly more training, but is relatively intuitive. Continued
31 Table 3 continued
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Rapid Presence/absence Plot to larger Time taken to Can be carried Inductive is the (Tzoulas and assessments and often numbers of scales complete the out by greatest James 2010) elements in a survey, to individuals with concern, predefined list. longer periods minimal although (Herzog, Kessler, These metrics are with repeated training. statistical and Cahill 2002) then generally measures Cost of survey sampling bias combined to give a (although development are also (Duelli and score of area frequently can be high, but possible. Obrist 2003)
32 diversity. While the repeating subsequent results may not be these studies deployment is (Kerr, Sugar, exact, they can show can be less. Packer 2000) trends in population impractical). numbers, and can identify areas for further study. Continued
32 Table 3 continued
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Remote From satellite (or Habitat to Limited to the The cost of Inductive bias (Gillespie et al. species aerial photography) regional time the capturing or is a major 2008) mapping mapping of the scale image was acquiring concern, as this (plant) distribution of taken, to images may be method (Nagendra et al. specific species, longer periods quite high, and assumes that 1999) which are large from repeated thus may make the diversity of enough to identify measures. repeated these lower from a distance. Depending on measures trophic levels Generally larger the imaging impractical. is always 33 plants. method, Varying degrees representative
frequent of training are of total repeated required to diversity. measures may identify or Statistical, and be classify plants sampling are impractical. from these also possible. images, but generally lower levels than traditional methods. Continued
33 Table 3 continued
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale NDVI The relative Habitat to Limited to the Depending on Inductive is the (Pettorelli et al. presence/abundance global time the the cost of the greatest 2005) of live green image was images, may be concern, vegetation from taken, to cost/time although (Gillespie et al. remote images. longer periods efficient statistical and 2008) from repeated estimate. sampling bias measures. Analysis of are also Depending on images is possible.
34 the imaging largely method, automated. frequent repeated measures may be impractical. Continued
34 Table 3 continued
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Red List Trends and changes Ecosystem- Based on at Depending on Statistical, (Butchart et al. Index in the status of global scale. least two the ease of inductive, and 2005) groups of threatened repeated locating, sampling bias species. measurements identifying, and can affect these (Hoffmann et al. (corrals, mammals, of the same counting the studies, 2010) birds, amphibians, group. species, especially etc) monitoring red given the rare list species may nature of these be much more species. 35 cost efficient
than other methods. This method, however, may still be quite labor intensive. However, because of the threatened nature of these species, it is often easier to justify and fund such studies. Continued
35 Table 3 continued
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Living Planet Trends in abundance Primarily Based on Data (Loh et al. 2005) Index and distribution of global, but repeated availability selected vertebrate can be measures Statistical, (World Wildlife species. calculated since 1970. inductive, Foundation ) Similar to a stock for smaller sampling market index, regions. changes in each species’ population
36 are aggregated and reported relative to their levels in 1970. Calculated for Global, Temperate, and Tropical. Continued
36 Table 3 continued
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Extrapolation Numbers of Range in Period during Many are now Presence-only (O'Connell, from natural specimens present in which which digitally Sampling bias Gilbert, Hatfield history museum collections specimens specimens searchable, Gaps in time 2004) collections or records from past were were centrally and space of studies to determine collected. collected. located, already data (Ponder 1999) historical range and Plot to identified and Unknown diversity. global scale. organized. collection (Stockwell and
37 Additionally, this Manual procedures Peterson 2002)
technique can use searches, Ad-hoc nature known indicators however, are of collections FROM biodiversity still labor Taxonomic to extrapolate intensive, can be bias. environmental data quite costly, and in past. may require a Can create good deal of background data training. from other Able to validate specimens to test the later (i.e. sampling effort of permanent the collection. record).
Continued
37 Table 3 continued Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Landscape From aerial Landscape, Depends on Cost of Inductive is the (Duelli 1997) mosaic photographs, the to regional the photographs and greatest pattern number of biotope persistence of computational concern, (Jeanneret, types, number of the mosaic software can be although Schupbach, Luka (Land Cover) habitat patches, pattern, but high. statistical and 2003) length of borders generally the Specialists are sampling bias (ecotones) and period in needed to are also (Gillespie et al. surface area ratio of which the perform possible. 2008)
38 natural to semi- image used analysis,
natural to cultivated was taken. although land. Repeated automation is The average measures are possible in biodiversity value possible, but theory. for each habitat type may not be However, if should be known. practical. correlated with total diversity, assessing the landscape mosaic pattern in photos can be less costly and time consuming, and require less training for specialists than traditional methods Continued
38 Table 3 continued
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Ecosystem The diversity of Regional to Time scale is Cost is Statistical, (Duelli 1997) Diversity ecosystem types in a global dependent on dependent on inductive, given area, which the stability of the wide variety sampling. may be related to the the ecosystem of methods total diversity of life. types. possible for assessing ecosystem
39 diversity.
Regression The level of a Habitat to Instantaneous Cost/time Inductive is the (Gaston and Models: parameter that is a global. to repeated, efficient. greatest Blackburn 1995) Level of statistically depending on May be able to concern, environmental significant correlate the method. use already although (Bailey et al. parameters with variation in available data. statistical and 2007) species richness. sampling bias (Such as: Latitude, are also Net Primary possible. Production (NPP), and Total Solar Radiation (TSR), as a predictor for species richness of New World birds.) Continued
39 Table 3 continued Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Physical The Habitat to Depends on Cost to train and Inductive is the (Heino and surrogates for presence/abundance global. the method employ greatest Mykra 2006) biodiversity of physical used. specialists to concern, parameters that are perform analysis although (Post, significantly may be high and statistical and Wassenberg, correlated with procedure can sampling bias Passlow 2006) biodiversity. While be time are also not the best consuming. possible.
40 indicator, may be Cost effective in
good for preliminary areas where studies to indicate surrogates show where further work low is needed. heterogeneity (Such as tributary within groups, and stream type and/or high classification as a heterogeneity surrogate of macro between groups. invertebrate Additionally, a assemblages in low number of running water.) easily distinguishable classes that reflect ecologically relevant factors for biota, are necessary. Continued
40 Table 3 continued
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Habitat The condition of a Habitat Sampling Inductive bias (see above) condition habitat, linked to period, is of the largest certain parameters, although concern. which generally repeated Although indicate the level of sampling is statistical and diversity present in possible. sampling bias the system. are also possible.
41 Assumes that
the parameters used to indicate the condition of the habitat are always, and predictably linked to biodiversity. This, however, is not fully proven, or infallible for many parameters used. Continued
41 Table 3 continued
Method Actually Measured Spatial Temporal Time/Cost Bias Key Citations Scale Scale Management Portion of land under Regional to Sampling Cost, time, and Assumes that (Kadoya and intervention specific land global period, training are management Washitani 2011) management strategy although limited by only strategy is (For some repeated needing to always, and management types sampling is identify the area predictably (i.e. certified possible. of land under a linked to organic) there is a particular type biodiversity. considerable amount of management. This, however,
42 of research is not fully
indicating that land proven, or managed as such infallible for does have higher any diversity levels of management some taxonomic strategy. groups)
42 Although these measures address many aspects of biodiversity, because of the drawbacks mentioned, they often fail to provide data that can easily be compared with data collected in the past or using other measures. Many scientists have expressed a need for a new scalable approach to monitoring biodiversity that takes advantage of recent advances in technology, which could allow us to quickly identify and respond to ecological changes (Naveh 1994; O'Neill 2008). Rapid baseline monitoring could provide scientists with the ability to determine the rate at which the environment is changing and where. Additionally, such monitoring could help scientists to determine critical tipping points, and the effects of such changes on ecological services (Dobson 2005). To fulfill these requirements, data must be collected in near real-time, and assessment methods must be carefully standardized (O’Neil 2008, (Dobson 2005; Porter et al. 2005). Such a measure of biodiversity might be able to support the effectiveness of conservation farming measures, and inspire the kind of confidence in results that would allow such practices to become widespread (Naveh 1994).
One proposed method, remote sensing, can easily provide biodiversity information on very large spatial scales. In comparison with traditional data collection methods, remote sensing can provide inexpensive spatial coverage of wide areas, in a consistent manner, with easily updateable data to monitor non-stationary relationships
(Gillespie et al. 2008). Satellites are increasing used to remotely monitor changes in non- mobile species like plants. In many studies, the diversity of plant land cover, often estimated from satellite images, is used as a proxy for total biodiversity (Vadrevu et al.
2008), Gillespie 2008). While such remote monitoring of plants can provide an idea of actual area plant diversity, it is often quite difficult to accurately identify plant species
43 from these images, let alone determine the total plant biodiversity of the area represented
in the image. Such measures are based on the assumption that large amounts of plant diversity will support large amounts of higher trophic level diversity (Gillespie et al.
2008; Vadrevu et al. 2008). Similarly, diversity of higher tropic levels is sometimes used as a proxy for total biodiversity based on the assumption that these high levels of upper trophic level biota require high levels of lower trophic level biota to survive (Dobson
2005; Spangenberg 2007). Most animal species, however, are too small and move too quickly to be effectively monitored with satellite imagery (Gillespie et al. 2008).
1.4 Bioacoustics
One way of measuring biodiversity that may escape some of the biases of other methods is the use of an indirect cue of biodiversity, such as the sound produced by various biota (Sueur et al. 2008; Kasten, McKinley, and Gage 2007). Ecologists have long used bioacoustics, or the sounds animals produce intentionally when communicating or sensing their environment, or incidentally when moving, as a means of assessing the presence and abundance of species (Ferrington 2000; Sueur et al. 2008). Such acoustic signals have been used for decades in biological censuses such as the North American
Breeding Bird Survey (USGS 2007), and the North American Amphibian Monitoring
Program (Corn et al. 2005; REAL ), and to make maps of populations and migratory patterns (Porter et al. 2005). Traditional methods of sound sampling, however, are limited because a human listener can only be at one place at a time, and their presence can alter the environment (Porter et al. 2005). Although the use of sound to estimate biodiversity does not account for plant, microorganisms, or other silent species, it does provide a
44 comparative indicator of the relationship between different groups as well as the impact of events on ecological communities (Kasten, McKinley, and Gage 2007; Lammers et al.
2008; Sueur et al. 2008). Unaided acoustic studies, where only the human ear is used to determine presence and abundance, however, still face many of the opportunities for bias seen in other sampling methods.
The use of handheld recording devices alone or in combination with traditional methods, can reduce the subjectivity of sound based studies. Handheld recording devices can increase the accuracy of sound sampling by allowing researchers to reexamine recordings later to verify their identification of acoustic signals (Celis-Murillo, Deppe,
Allen 2009; MacSwiney G.; Clarke, Racey 2008). Additionally, recording can be analyzed with programs that allow researchers to identify sound signatures that are out of the range of normal human hearing (MacSwiney G.; Clarke, Racey 2008). Furthermore, technology is being developed that uses computer algorithms to accurately identify the song signatures of certain species automatically (Chesmore and Nellenbach 2001;
Kasten, McKinley, and Gage 2007). Such technology has been used in studies of orthopterans (Chesmore and Nellenbach 2001), birds (Celis-Murillo, Deppe, Allen 2009;
Kasten, McKinley, and Gage 2007), bats (MacSwiney G., Clarke, Racey 2008), and amphibians (Acevedo and Villanueva-Rivera 2006). Because of the greater efficiency with which data is collected, the increasing availability of high quality/inexpensive recording devices, and the decreased need for skilled taxonomists in the field, the use of such techniques may be more effective, easier, and less expensive than many traditional sampling methods (Celis-Murillo, Deppe, Allen 2009; Kasten, McKinley, and Gage
2007; MacSwiney G., Clarke, Racey 2008; Sueur et al. 2008).
45 A good example of the recent advances in biodiversity studies using hand held recording devices is a 2008 study by Sueur et al., which compared multi-genera diversity between habitats. The study used sound to measure both ! and " diversity in two
Tanzanian forests with different levels of human intervention, using recorded sound. The algorithms used in this study were based on the assumption that as the number of species in a community increased, more sounds would be produced, thus increasing the heterogeneity of the soundscape through the partitioning of acoustic space. This theory and the two indexes (! and " diversity) were validated using simulated acoustic choruses, prior to their use in the field. This showed that as heterogeneity of sound emitted by the community was indeed positively correlated with the number of species present (!- diversity), and that acoustic dissimilarity could be detected between the sounds produced by communities with increasing numbers of unshared species ("-diversity). In the field, the study found that differences in both ! and " diversity could be determined between intact and degraded forests using recorded sound. The intact forest showed higher !- diversity levels than the degraded forest, and the "-diversity index showed clear differences between the two habitats. Such methods, however, still do not escape the possible effects of human presence altering the system, or the limitations of scale tied to human mediated testing.
Use of remotely recorded sound to monitor ecosystem function, although only recently attempted, is yielding intriguing results. In comparison to hand held microphone sampling, automated microphone arrays (often called ecological sensor networks or ecological acoustic recorders (EARs)), can be synchronous, multipoint networks that unobtrusively monitor soundscapes (Kasten, McKinley, and Gage 2007; Lammers et al. 46 2008; Porter et al. 2005). These networks can gather large volumes of data, without requiring scientists to constantly remain in the field (Acevedo and Villanueva-Rivera
2006; Cai et al. 2007; Hutto and Stutzman 2009; Lammers et al. 2008). Additionally, by removing the human observer from the field, these studies can eliminate the source of error that could be caused by inter-observer error and the observer’s presence in the environment (Celis-Murillo, Deppe, Allen 2009; Hutto and Stutzman 2009; Porter et al.
2005). Microphone arrays can often be set up quickly and easily by those with minimal training. The data provided by these networks can potentially show the type of event, identify species, and discern community composition and abundance (Cai et al. 2007;
Celis-Murillo, Deppe, Allen 2009; Kasten, McKinley, and Gage 2007Lammers et al.
2008; Porter et al. 2005). Additionally, sensor networks can provide data on daily, seasonal, and annual cycles in multiple locations, and can facilitate reliable, inexpensive data collection, as well as collection at inconvenient or dangerous times, and at greater temporal and spatial scales than traditional methods (Acevedo and Villanueva-Rivera
2006; Cai et al. 2007; Kasten, McKinley, and Gage 2007; G. Sanchez, R.C. Maher, and
S. Gage ; Hutto and Stutzman 2009; Lammers et al. 2008). Such systems provide the technology to increase the spatial and temporal scale of monitoring while reducing human labor and error (Celis-Murillo, Deppe, Allen 2009; G. Sanchez, R.C. Maher, and
S. Gage ), and when combined with ancillary measures this data can yield a large volume of ecologically relevant information (G. Sanchez, R.C. Maher, and S. Gage ; Kasten,
McKinley, and Gage 2007; Lammers et al. 2008; Porter et al. 2005; REAL ). Thus these systems have the ability to reveal previously unobservable phenomena. Furthermore, the recordings produced by these systems can serve as a ‘permanent record’ of a study,
47 which can be used to reexamine results and even answer new questions as they arise
(Acevedo and Villanueva-Rivera 2006; Celis-Murillo, Deppe, Allen 2009; Kasten,
McKinley, and Gage 2007).
Additionally, sound can be used to analyze the relationship between humans and animals by showing the effect of human made sound on biotic communities. Such data can be used to measure biological and anthropological patterns and interactions
(Pijanowski et al. 2007; Ferrington 2000; Kasten, McKinley, and Gage 2007; Lammers et al. 2008; Porter et al. 2005). This system has proved effective not only in monitoring biological activity and revealing temporal patterns in a location where constant monitoring by traditional survey methods is impractical, such as coral reefs (Lammers et al. 2008) and marshes (Cai et al. 2007), but has also proven effective as a method to analyze the effects anthropogenic disturbances have on biological communities (Cai et al.
2007; Lammers et al. 2008).
Remote monitoring of the soundscape, however, still faces many challenges.
Recording systems are sensitive to all sound, not only those that scientists are attempting to study (Chesmore and Nellenbach 2001; Sueur et al. 2008). Abiotic noise from elements such as rain and wind can often mask the sound signatures of the target subjects
(Forrest 1994; Kasten, McKinley, and Gage 2007). Acoustic filters can decrease, but not always remove these effects. Additionally, because of the immense amount of data generated, the majority of analysis must almost necessarily be done using computer algorithms, which can be more consistent but less adaptable than human analysis (Hutto and Stutzman 2009; Kasten, McKinley, and Gage 2007). Because of this, a significant
48 amount of time is required to develop programs that produce pertinent and consistent results (Kasten, McKinley, and Gage 2007; Porter et al. 2005; Sueur et al. 2008).
While still an imperfect technique, remote acoustic sensing’s responsive and easily comparable values can provide us with a valuable ability to compare ecosystems and to discern the effects of disturbances. Furthermore, the use of automated wireless networks could allow us to see these effects in near-real time, on previously impractical scales, and thus allow us to better understand underlying ecological relationships, or to mitigate disturbances before their effects become severe.
1.5 This study
Building on previous research, this study proposes that a novel means of monitoring agroecosystem biodiversity, and ultimately ecosystem health, by calculating both !-diversity and !-diversity as well as biological and anthropogenic interactions over time using remotely recorded sound, could overcome many of the issues seen with other measures of biodiversity, and could thus confidently support the causal connection between conservation farming methods and increased levels of biodiversity. As shown above, most previous uses of sound in ecological studies have been aimed at identifying species or behavior of one taxonomic group (Pijanowski et al. 2007). Additionally, these studies, and those that have looked at multiple species, are often conducted in systems like coral reefs (Lammers et al. 2008), tropical rainforests (Sueur et al. 2008), or marshes
(Cai et al. 2007) that society is trying to maintain in as pristine and “Natural” a state as possible. This study, however, attempts to determine the relationship between anthropogenic and biological sounds in a landscape that represents the interface between
49 man and nature. Specifically, it attempts to show divergent levels of diversity in different agricultural settings and to test for a threshold level of anthropogenic noise above which biological sound significantly decreases.
This study proposes to compliment the previous work done to create the
Agroecosystem Health Index (AHI) (Vadrevu et al. 2008). The calculation of the AHI is very data intensive and provides a landscape scale measure and map of ecosystem health at a point in time. The AHI is calculated from six component variables: plant landscape diversity (BD), soil quality (SOIL), topography (ELV), social organization (SOC), farm economics (CAUV), and land economics (MKT) (Table 4). The resulting index produces a value for each variable and the AHI for each 30 m2 pixel analyzed. The pixel value for each variable, however, is determined slightly differently. AHI, BD, and ELV are a function of a 12 x 12 pixel moving window around the pixel. SOIL, on the other hand, is a function of only the conditions within the pixel. The remaining social variables (SOC,
CAUV, and MKT) are the same for every pixel in a parcel.
50 Table 4. Summary of the key variables and data used to describe Agroecosystem Health. (Vadrevu et al. 2008)
This study compliments the AHI by remotely collecting data on the relationship of anthropogenic and biological communities at a specific place but over a larger time scale. Additionally, the measure of biodiversity proposed here may be a more accurate or complete representation of biodiversity, than the measure of plant community heterogeneity calculated from aerial photographs currently used in the AHI. As with the land cover technique, this method does not attempt to identify and count individual species, due to the complexity of acoustic signals. Rather, this method examines how complete the acoustic soundscape is, which would relate to how many different sounds and sound patterns are occurring, and compares the soundscape patterns of different locations. Additionally, by not focusing on a specific species or genera of biota, this technique examines a more ecologically relevant taxonomic range than many methods that measure only one group of organisms (Dobson 2005). Although similar to Sueur et al. (2008), my research uses remote recordings, and seeks to determine the relationship
51 between anthropogenic and biological sound in agricultural ecosystems, rather than merely biological sound in natural habitats, and examines a much larger temporal scale.
1.6 Objectives and Thesis
My thesis is that recorded sound can be used to measure biodiversity levels as accurately or more so than traditional measures. Furthermore, I predict that this data will show a threshold level of anthropogenic sound (likely associated with management type) above which diversity of natural sounds will significantly decrease. In the following chapters, I will support this thesis with results of research on the relationship of sound from agricultural management practices and that of biotic communities in agroecosystems that focused on the following questions:
1. Is there a significant relationship between the acoustic entropy (H’) of sites and
their agroecosystem health index values, and/or the AHI’s component variables
(plant landscape diversity (BD), soil quality (SOIL), topography (ELV), social
organization (SOC), farm economics (CAUV), and land economics (MKT)).
2. Does the sound index measure biodiversity levels comparable to those found
using other measures of biodiversity?
3. How does the sampling radius of remote acoustic monitors compare to the
sampling radius of other methods of measuring biodiversity? (spatial auto
correlation)
4. What is the length of sampling time needed to consistently show significant
differences between sites?
52 5. Can the H’ index be used to identify anthropogenic acoustic disturbances, and do
high and low AHI sites respond differently to these disturbances?
53
Chapter 2: The Modified Acoustic Entropy Index as a Measure of Biodiversity
2.1 Introduction
Biodiversity, defined as “the variability among living organisms from all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part… [including] diversity within species, between species and of ecosystems” (United Nations 1992), is critically linked to the functioning of ecosystems. As such, biodiversity is necessary for the support all life on earth, including our own. Global biodiversity, however, is declining at a staggering rate (United Nations
1992). In order to understand and halt or reverse these declines, we must be able to monitor changes in biodiversity levels, as they happen.
A wide variety of methods for measuring biodiversity exist, each with their own advantages and weaknesses, including differences in scale, cost, and level of bias.
Although these measures address many aspects of biodiversity, because of the drawbacks mentioned, they often fail to provide data on a scale that can easily be compared to data collected in the past or using other measures. Many scientists have expressed a need for a new scalable approach to monitoring biodiversity that takes advantage of recent advances in technology, which could allow us to quickly identify and respond to ecological changes (Naveh 1994; O'Neill 2008).
54 Rapid baseline monitoring could provide scientists with the ability to determine the rate at which the environment is changing and where. Additionally, such monitoring could help scientists to determine critical tipping points, and the effects of such changes on ecological services (Dobson 2005). To fulfill these requirements, data must be collected in near real-time, and assessment methods must be carefully standardized
(Dobson 2005; O'Neill 2008; Porter et al. 2005). Such a measure of biodiversity might be able to support the effectiveness of conservation farming measures, and inspire the kind of confidence in its results that would allow such practices to become widespread (Naveh
1994). Monitoring biodiversity through the use of remotely recorded sound, is one method that has been proposed to fulfill these requirements (Cai et al. 2007; Lammers et al. 2008; MacSwiney G., Clarke, Racey 2008; Porter et al. 2005; REAL ; Sueur et al.
2008).To develop a method of monitoring biodiversity using remotely recorded sound, investigations were conducted in Wooster, Ohio to compare the acoustic variability
(using the modified acoustic entropy index (H’)) of locations that differed in
Agroecosystem Health Index (AHI) values, and to compare remotely recorded sound with several traditional methods of biodiversity assessment.
To develop an accurate comparison of the H’ index with other biodiversity assessment methods, it is necessary to determine the Acoustic Entropy Index’s ability to incorporate animals with particular vocalization frequencies and amplitudes into its calculation of biodiversity. To do this, we must determine the ability of the recording apparatus used (in this case the SongMeter SM1) to detect sounds with different frequencies and amplitudes. In the absence of all factors other than the resistance of air, the change in amplitude of a sound between the sender and the receiver is determined by
55 spherical loss (where amplitude decreases 6 dB for every doubling of distance) (Forrest
1994). Excess attenuation, the loss of amplitude beyond that predicted by spherical spreading, is caused by interactions of the sound wave with the environment through which it travels, including: absorption, scattering, reflection, and refraction (Forrest
1994). The predictable nature of these interactions, when combined with natural selection and resource competition, has led animals to evolve vocalization patterns that optimize the distance their signal needs to travel in their habitat, metabolic effort required to produce vocalizations, while reducing the level of potential vocalization masking by other organisms (Forrest 1994; Marten and Marler 1977). Together these factor lead to the predictable frequency range of particular animal vocalizations (Forrest 1994; Marten and Marler 1977). Based on these predictable characteristics, and the sensitivity of the recording unit, the effective sampling radius of the sound sensor can be determined and compared with the sampling radius of other traditional methods of assessment (Table 5).
56 Table 5: Method of biodiversity assessment with actual indicator measured and relevant scale of measurement. Method Measured Scale AHI: Satellite imagery of Landscape plant 180 m patch, diversity among plant community heterogeneity 30m x 30m pixels Plant Transect Plants touching the tape 12 meters x 15 centimeters Insect Pitfall Primarily ground insects Between 0.5 and 23.5 m per trap (~25.5 m max how is this being calculated/estimated??) Bird Calls Vocalizing birds Theoretically, 15 m to over 1,000 m (although detection is highly dependent upon the listener’s ability to distinguish vocalizations in the recording). Acoustic Entropy Sound producing biota 15 m to over 1,000 m
2.2 General Methods
2.2.1Study Area
Ohio is an excellent location for research on biodiversity within agroecosystems, because it has both a diverse agriculturally based economy and a large well distributed population. This study uses a sampling area that was the focus of a case study of developing an agroecosystem health index (AHI), because of the wealth of landscape level data that already exists on the area from previous research (Vadrevu et al. 2008), and because of the perceived gradient in farms and surrounding landscape elements. This approximately 11 square mile area is located just south of the Ohio Agricultural Research and Development Center (40º47’ N latitude, 81º55’ W longitude), in Wayne County,
Wooster, Ohio, USA (Figure 1). From west to east in the study area, field size decreases, crop diversity increases, pastures and livestock increase, and the mixture of woodlands and other landscape elements with agriculture increases (Vadrevu et al. 2008). Six sites 57 that were hypothesized represent a range of management types, and thus a range of sound relationships, based on differences in the previously calculated AHI and component variables were identified for use in the study. The distance between sites ranged from approximately 510 m to 6200 m.
Due to the nature of site selection (using sites in the AHI study area) and because permission had to be obtained to place sensors on privately owned farms, the site selection process was non-random. The sites chosen, however, do cover a range of management styles present in the area, and values of the agroecosystem health index and its underlying variables. The sites used are described below. OH20, OH21, OH24, OH25, and OH22 are intensively managed typical Ohio mechanized grain farms. OH20, OH21,
OH24, and OH25 are in corn-soy rotation. The farmed area in OH20, is quite large while the area of the adjacent site, OH21 is much smaller. OH24 is a series of smaller corn plots within the OSU sheep unit, and are thus surrounded by active pasture. The lanes between these plots are repeatedly mowed, and the area experiences a good deal of human traffic. Additionally, this is the only testing location that is not on a tree line.
OH25 was planted in wheat in 2009 and clover in 2010, and does not have actively maintained fence lines, and experiences very little human traffic. Additionally, this is the only testing location that is located in close proximity to a stream. OH23 is an intensively managed typical Ohio mechanized grain farms style farm in corn-soy rotation.
Furthermore, this site has mowed lanes along its tree line, where the sensor was placed, and experiences a good deal of motorized human traffic. The site that differs the most from the group is OH22, which is an Amish farm that has not used chemical pesticides or fertilizers, since at least the early 1900s when the family acquired the land. All work on
58 the property is done by hand and with the assistance of horse drawn equipment. The area between the edge of the crop and the tree line is not actively maintained.
At each site, a sensor platform was affixed to a six-foot t-post and placed adjacent to crops. All but one platform was placed on tree lines. Sensor platforms were in place from 6 August 2009 to 4 November 2009 and 24 April 2010 to 8 October 2010.
20 21 25 24 23 22
Figure 1. Study area. Black dots indicate test sites. Numbers below identify site number.
2.2.2 Sensor Platforms
Commercially available acoustic monitoring platforms (Song Meter SM1,
Firmware version 1.7.0, Wildlife Acoustics) were chosen for use in this study, because of their easily customizable programming, upgradeable memory, and relatively long battery life. Additionally, these units are relatively inexpensive ($600 at the time of this study).
59 These units are powered by four D-cell batteries, and record .wav files onto SD cards.
Units were programmed using their companion software, Song Meter Configuration
Utility 1.9.2. This intuitive software allows the user to set recording times and lengths, sample rates and compression values, as well as to predict when memory and battery will run out.
The sensor platforms were programmed to record for 30 seconds every half hour and to enter a power saving sleep mode when not recording, consistent with recording schedules used in previous acoustic monitoring studies (Kasten, McKinley, and Gage
2007). Units were set to record from only the right channel, with + 45 db gain and no compression, at a sampling rate of 22050 samples per second. Data was manually collected from SD cards and uploaded to a laptop computer every few weeks, although the units can be left for up to three months before running out of battery power.
2.3Comparison of the Modified Acoustic Entropy Index (H’) and Traditional Measures of
Biodiversity
2.3.1 Calculation of the Modified Acoustic Entropy (H’)
Recordings were analyzed using custom software developed using Matlab
(version 7.7.0.471 (R2008b)) (Software developed with Michael Miller, 2008). The analysis of recordings was based on the acoustic entropy index (H) developed by Sueur et al. (2008). The Sueur index is based on the product of two equations (Ht and Hf) derived from Shannon’s Diversity Index. The Shannon Diversity Index, the second most used index of diversity, is calculated for a set of categories of differing frequencies, and
60 increases as the number of categories and the evenness of their frequencies increase.
Thus, when we assume that species equate with categories, Shannon’s Index increases as
the number of species increases and their relative abundance becomes more even. In the
Sueur index, this theory is applied to time series of length n, where the categories are
time units, and their frequencies are the probability mass function of the amplitude
envelope. Here, the amplitude envelope is used to describe the variation in amplitude of a
wave that varies in time and/or position, in contrast to a continuous periodic wave, where
the descriptive parameters of the wave are fixed and known for the entire sample.
For the time series x(t) of length n, the amplitude envelope of oscillation is
calculated from the analytic signal "(t) of x(t), where "(t) is defined as:
"(t) = x(t)+ixH(t) (1)
2 where i =-1 and xH(t) is the Hilbert transform of x(t).
The probability mass function of the amplitude envelope A(t) is then calculated as,
"(t) n , such that (2) A(t) = n " A(t) =1 # "(t) t=1 t=1
The Sueur index then calculates the temporal entropy (H ), that is the variation in ! t ! the evenness of the amplitude envelope A(t) over the time units of the sample, using the
formula,
n -1 Ht = - # A(t) * log2 A(t) * log2 (n) , with Ht $ [0,1] . (3) t =1
61 Following this formula, Ht will be high when there is a high level of amplitude modulations in the temporal spectrum. This number is loosely comparable to the number and evenness of types of individuals in the sample.
Similarly, spectral entropy (Hf), or the variation and evenness of frequencies in the spectral range of the sample, is calculated for the probability mass function S(f) of length N using the formula
N -1 Hf = - # S(f) * log2 S(f) * log2 (N) , with Hf $ [0,1] . (4) f =1
Using this formula, Hf will be high when a large number of different frequency bands are represented in the sample and the number of instances of each frequency is similar. As with Ht, this number is used as an approximate representation of the number of species in the sample.
The total entropy (H) is a product of these temporal and spectral entropies
H = Ht * Hf, with H $ [0,1]. (5)
Thus, using this equation, H will be relatively high when both the temporal entropy (Ht)
(amplitude modulations), and the spectral entropy (Hf) (the number of different frequency bands represented) are high. H will approach one, representing white noise, when there is total coverage of the spectrum. That is when a great number of frequency bands (equated with species) are present and vary greatly in amplitude (equated with individuals) throughout the recording. A decrease in either the number of frequencies (species), or variation in the amplitude envelope (individuals) should decrease H.
62 Sueur et al. (2008) tested this formula using a simulated chorus composed of different numbers of species. This was achieved by combining varying numbers of recordings randomly selected from a pool of 45 species. Ten recordings were created by successively adding species leading to recordings with one species to recordings with ten species. The results of this test, and subsequent use in the field supported Sueur et al.’s hypothesis that the index produces values that tend toward 0 for no noise, and values close to 1 for white noise, or total saturation of the acoustic environment.
After initial testing of the Sueur index (H) on my 30-second recordings, I found that the index was producing similarly high values for recordings with very little sound
(such as quite nights registering mainly microphone hiss) and loud events like rain
(which approximates white noise, by producing sounds with large variations in frequency and amplitude). While to the human ear and eye, these spectrums are clearly very different, they produced similarly high index values because both of these situations produce similar evenness in the sound spectrum. To remedy this, I scaled the Sueur index value by multiplying it by the average absolute value of the amplitude for each recording
(which falls between 0 and 1). The resulting equation ensures that the acoustic entropy values will increase in this order: recordings with little to no sound < recording with occasional loud sounds < recordings with even and loud sounds. This modified index, however, is still driven by differences in the temporal and spectral entropy of the recordings. The greater responsiveness of this scaled acoustic entropy index was judged to be more valuable than the possible error introduced by this operation.
Finally, while Sueur et al. (2008) used their index to monitor the soundscape in ecosystems that are largely devoid of sound produced by humans, agroecosystems where
63 this study was conducted often contain large amounts of sounds associated with human communities. This biological-anthropogenic interaction is a driving force in determining community composition and function in agroecosystems. Monitoring of biological sounds in agroecosystems, therefore, may be a good method of accessing the response of these communities to anthropogenic activity. In order to ensure that my index is responding to sounds produced by the animal community, I chose to filter out the range of sounds associated with humans, which from prior research (Forrest 1994) and analysis of my recordings, I found was between 0 and 1 kHz. By filtering out this lowest kilohertz, which is often quite loud, I also decreased the amplitude of the scaling factor and thus further reducing the effect of anthropogenic noise on the final index value. By this scaling and filtering of the Sueur index (H), I created the Modified Acoustic Entropy
Index (H’). Custom Matlab software was used to analyze each recording, and produced an Ht’, Hf’ and H’ value for each file. The resulting daily and seasonal patterns from each scaled component (Ht’, Hf’) and the final H’ index were examined individually to determine if either component provided a good representation of total variation in the soundscape, or if emergent properties were seen in the H’ index, that were not evident in either individual component. Additionally, the waveforms of selected recordings were inspected with Raven Pro (version 1.3) to examine patterns in more detail, and to ensure that the index was functioning as expected.
2.3.2 Calculation of Traditional Measures of Biodiversity
To validate the modified acoustic entropy index’s ability to react to biodiversity levels, H’ values were compared with those of traditional biodiversity measures. The
64 three methods chosen are representative of some of the most commonly used biodiversity assessments, and their efficacy is well represented in the literature. Additionally, they cover a range of taxa that are often used as indicators: plants (Gillespie et al. 2008; Santi et al. 2010; Tilman, Reich, Knops 2006), ground dwelling insects (Fournier and Loreau
1999; Holland et al. 2005; Isaacs et al. 2009), and birds (Celis-Murillo, Deppe, Allen
2009; Gaston and Blackburn 1995), and require, as nearly as possible, similar levels of training/expertise to conduct.
Plant Line-Intercept Study
The line-intercept method was used to quantify plant diversity around each sensor on August 10th or 11th, 2009. A contractor’s measuring tape was run six meters in front of the sensor unit, towards the field, and six meters behind the sensor unit, towards the woods. Each plant that came in contact with the tape was then identified and counted.
This was repeated at each site. This data was then analyzed with Shannon’s Diversity
Index for each site (Bonham 1989).
Insect Pitfall Study
The insect pitfall trap method was used to quantify ground-dwelling insect diversity around each sensor between September 4th and 14th 2009. At each site, three
16oz cups were buried to their mouths, each equally spaced and 1 m from the sensor. In each of these cups was another cup filled with approximately 2 inches of a mixture of dish soap and water. Because of the location of the traps on farmland, and the danger of domestic animals consuming the solution, no preservative or attractant was used. The
65 cups were then coved securely with 1inch poultry netting, to prevent contamination by debris and interference by other animals. This was repeated at each site. The insects in the traps were collected twice, at 10-day intervals. The insects collected from each site were then identified to genus and counted (Sutherland 2006). Shannon’s Diversity Index was calculated using these data for each site.
Avian call Study
A single Song Meter Unit was used as a hand held device to record bird calls four times at each site, on September 9th, 2009 and September 14th, 2009. Two, 5-minute recordings, with a two minute gap, were recorded at each site. These recordings were visually analyzed with Raven Pro software. Individual waveforms for birdcalls were counted. Because of the difficulty in identifying calls, no distinction was made between species or individuals. The number of calls was averaged between the two recordings for each site.
This method is not very robust as a measure of bird species diversity, but did provide another comparison with the results of the acoustic index, and requires a similar level of training to perform. It is quite difficult for a trained ornithologist, let alone a novice birder, to distinguish between bird calls in recordings. More robust methods, carried out by trained professionals, which use recorded bird calls for species identification and diversity estimates, in comparison with traditional point count methods, are discussed in detail in the literature (Acevedo and Villanueva-Rivera 2006; Celis-
Murillo, Deppe, Allen 2009; Herzog, Kessler, Cahill 2002; Hutto and Stutzman 2009)
66 2.3.3 Determination of AHI and Component Variable Values
To determine the AHI and component variable values for each site, the geocoded latitude and longitude for each sensor unit were entered into ArcGIS (Version 10). The values for each of the six component variables and the AHI were extracted for two scales around each sensor: the pixel containing the sensor, and a 3 x 3 pixel area around the sensor. Thus, for each variable, a 30 m2 (0), and a 90 m x 90 m (3) average for each variable at each site was calculated and used in further analyses.
The resulting site averages for each variable, at each extent were then checked for correlation with each other in SPSS. Land economics (MKT0 and MKT3) is significantly correlated with social organization (SOC0 and SOC3) on all spatial scales tested. See appendix for specific values.
2.3.4 Sampling Radius of Acoustic Sensor
Each of the traditional methods of assessment described above estimate biodiversity within a well defined sampling area. Likewise, acoustic methods are associated with a sampling radius. The acoustic sampling radius, however, is potentially more complex, and is highly dependent on the particular recording device used to collect data. To determine the practical sampling radius of the Song Meter (SM1, Firmware version 1.7.0, Wildlife Acoustics) used in this study, two Song Meters were placed side by side, on t-posts, in a level open field and their ability to detect different frequency tones, at different amplitudes, at 10 m intervals between 10 and 150 m was tested. The tones tested were: 1,000 Hz, 5,000 Hz, 2,000 Hz, 1,000 Hz, and white noise. Each of these tones was played at each 10 m distance at seven amplitudes: 100 dB, 90 dB, 80 dB,
67 70 dB, 60 dB, 50 dB, and 40 dB. These tones and amplitudes were chosen to represent a wide range of possible biophonic sounds that might be encountered during testing, and to allow future researchers to more easily determine the practical sampling range for particular species with known frequency and amplitude parameters. The tested tones were computer generated test tones of equal amplitude. These tones were then combined into an .aiff file, which contained each sequence of tones repeated at each dB level. The amplitude of each tone, and the amplitude differences among sequences were verified using Raven Pro (version 1.3). The waveform and spectrogram of the test sequence is shown in Figure 2.
Figure 2. Wave form and spectrogram of the test sequence tones. Each band represents a tone, each group of tones represents a 10 dB step. Color in the spectrogram represents the amplitude of the tone.
68
Playback of tones took place over two consecutive days in the same field. Ten meter distances were measured and marked using surveying flags to ensure consistent distances, with the two SongMeters at 0 m and the furthest flag at 150 m. The tone series was produced by an MP3 player, connected to a set of high quality portable speakers.
Prior to testing each day, the amplitude of the first set of tones was verified to be 100 dB, using a hand held amplitude meter held approximately 30 cm from the speakers.
Following this verification, the test sequence was played at each 10 m interval, and recorded by the SongMeters. For each play back the speakers were held level with and facing the recording unit, to avoid decreases in amplitude associated with directionality of the sound sources. Two recordings were made on the first day of testing, and four were made the following day.
Following sampling, the recorded amplitude (dB) of each tone at each 10 m interval was calculated using a moving selection window and the Max Power (dB) function in Raven Pro (version 1.3). These values were then compared in SPSS Statistics
(version 17.0), by averaging the observed amplitude for each tone at each amplitude, at each distance. In some cases, a tone was detected by only one SongMeter for a particular amplitude and distance combination. In these cases, the value was excluded from calculation. The majority of values used for calculation were the average of four to six recordings.
69 2.3.5 Data Analysis
Statistical comparisons of the modified acoustic entropy index and the traditional biodiversity methods were performed with SPSS Statistics (version 17.0). The mean
Acoustic Entropy (H’) value for each site was calculated by averaging the H’ values for each of the 48 daily recording periods to create a mean H’ value for each time. These values were then averaged to create a mean daily H’ value for each site. The daily mean values were then averaged again to create yearly mean H’ values for each site and then compared to the AHI and the six component variables (plant heterogeneity (BD), soil quality (SOIL), social organization (SOC), topography (ELV), land economics (MKT), and farm economics (CAUV)) at both spatial extents (0 and 3) from each site, using standard linear regression. Additionally, the H’ index values were tested for association with the three traditional measures of biodiversity at each site, suing bivariate correlation.
Temporal Entropy (Ht’) and Spectral Entropy (Hf’) values for each site in 2009 were similarly calculated and analyzed. Preliminary inspection of the average half-hourly data suggested that differences in H’ between sites varied over the course of the day, perhaps related with temperature (Fig. 3) and other conditions, such as photoperiod.
70
Figure 3. Average daily temperature for the months during which testing took place in 2009 (OARDC Weather Systems 2011).
Recordings from 8 August 2009 – 4 November 2009, excluding two days during which I changed the SD cards, were used for calculation for all six sites in 2009. In 2010, technical issues with several of the sensors made much of the data absent or unusable for some sites during the testing period. To utilize as much of the data as possible for each site, without compromising the analysis, I included only dates for which continuous data was available for each sensor in my analysis. Thus, in 2010, only data from 24 April to 5
May were included in the analysis for OH22 and OH23. Data from 24 April to 1 August were included in the analysis for OH20 AND OH21. Finally, data from 24 April to 8
October was included for OH24 and OH25. 71 Mean Shannon’s Diversity Index values for the plant transect, insect pitfall, and avian call number studies, were also compared to the total site average acoustic entropy values for 2009, 2010, and both years using bivariate correlation. Data from the sampling radius experiment was analyzed in SPSS. A linear regression of amplitude
(units) on log distance was used to estimate the effective sampling radius for all frequency-amplitude combinations. Finally, the mean Shannon’s Diversity Index values for the plant transect, insect pitfall, and avian call number studies, were compared to the
AHI and the six component variables at all four spatial scales with linear regression.
2.4 Results
Modified Acoustic Entropy Index
A significant relationship was found between total mean H’ (H’ALL 2009 and
2010 combined) and biodiversity (BD0, slope = 0.014, SE = 0.005, t = 3.125, p = 0.035), but no other component variables or the agroecosystem health index itself (Figure 4).
Similarly, a significant relationship was found between mean H’ in 2010 and biodiversity
(BD0, slope = 0.015, SE = 0.003, t = 4.325, p = 0.012; BD3 slope = 0.017, SE = 0.003, t
= 6.542, p = 0.003), but no other component variables or the agroecosystem health index
(Figure 5). No significant relationships were found between mean H’ in 2009 and any of the component variables or the agroecosystem health index (Figure 6) (see Appendix for regression tables). Furthermore, the mean H’ of sites from 2009 is not significantly related to the mean H’ of sites in 2010.
72 a) b)
!
c) d)
e) f)
g)
Figure 4. Comparison of the Acoustic Entropy Index from 2009 and 2010 combined and the component variables of the AHI index, as well as the AHI, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01). 73 ! !
Figure 5. Comparison of the Acoustic Entropy Index from 2010 and the component variables of the AHI index, as well as the AHI, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01) 74 a) b)
c) d)
e) f)
g)
Figure 6. Comparison of the Acoustic Entropy Index from 2009 and the component variables of the AHI index, as well as the AHI, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. 75
Furthermore, the diel patterns for H’ in 2009 (Figure 7), 2010 (Figure 8), and both years combined (Figure 9) look somewhat different. Diel patterns will be discussed further in Chapter 3.
Figure 7. Comparison of average half-hour Acoustic Entropy (H’) values for test sites in 2009.
76 Figure 8. Comparison of average half-hour Acoustic Entropy (H’) values for test sites in 2010.
77
Figure 12. Comparison of average half-hour Acoustic Entropy (H’) values for test sites in 2009 and 2010 combined.
Spectral and Temporal Entropy Indices
No significant relationships were found between Hf (Figure 10) or Ht (Figure 11) for 2009 or 2010 and any of the component variables or AHI. Futhermore, neither Hf nor
Ht in 2009 displayed the diel patterns evident in the H’ 2009 data.
78 a) b)
c) d)
e) f)
g)
Figure 10. Comparison of the Spectral Entropy Index from 2009 and 2010 and the component variables of the AHI index, as well as the AHI, at two scales (30 and 90 m2):: a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. 79 a) b)
c) d)
e) f)
g)
Figure 11. Comparison of the Temporal Entropy Index from 2009 and 2010 and the component variables of the AHI index, as well as the AHI, at four scales (1, 3): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. 80
Comparison with Traditional Methods of Biodiversity Assessment
There was no significant correlation between plant diversity and H’, as determined using plant transect methodology, and Shannon’s diversity index in 2009,
2010, or both years combined (Figure 12 a). Similarly, there was no significant correlation between the diversity of ground dwelling insects and H’, as determined using pitfall traps and Shannon’s diversity index in 2009, 2010, or both years combined (Figure
12 b). Finally, there was no correlation relationship between the number of bird calls and
H’ in 2009, 2010, or both years combined (Figure 12 c). The observed plants are listed in
Appendix B, and the observed arthropods are listed in Appendix C.
81
a) b)
c)
Figure 12. Comparison of the Acoustic Entropy Index and Shannon’s diversity index calculated for: a) plant transect (r = 0.316, n = 6, p = 0.542), b) ground dwelling insects (r = -0.142, n = 6, p = 0.789), and c) number of bird calls (r = 0.147, n = 6, p = 0.782).
While neither traditional measure was correlated with the Acoustic Entropy
Index, the Shannon’s Diversity Index values obtained from the plant transect study and the insect pitfall trap study, were also not significantly correlated with each other or the number of bird calls (Figure 13).
82 a) b)
c)
Figure 13. Comparison of: a) the plant transect Shannon’s diversity index and the insect pitfall trap Shannon’s diversity index values (r = 0.264, n = 6, p = 0.613). b) the insect pitfall trap Shannon’s diversity index values and the number of bird calls (r = -0.517, n = 6, p = 0.0.293), and c) the plant transect Shannon’s diversity index the number of bird calls (r = -0.743, n = 6, p = 0.090).
83
In several cases, however, the traditional assessment methods were significantly related to the AHI or one or more of its component variables. The plant transect measure
(Figure 14) was significantly related to the Agricultural Health Index (AHI0: slope = -
5.391, SE = 1.623, t = -3.323, p = 0.029). The insect pitfall measure (Figure 15) was significantly related to soil quality (SOIL0: slope = -1.227, SE = 0.398, t = -3.101, p =
0.036; and SOIL3: slope = -2.415, SE = 0.719, t = -3.358, p = 0.028). Finally avian call number (Figure 16) was significantly related with farm economics (CAUV: slope =
160.929, SE = 45.938, t = 3.503, p = 0.025). See appendix for regression table.
84 a) b)
c) d)
e) f)
g)
! ! !
Figure 14. Comparison of Plant Transect and the component variables of the AHI index, as well as the AHI index, at two scales (1, 3): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01). (See appendix for regression tables). 85 a) b)
! ! !
c) d)
e) f)
g)
Figure 15. Comparison of Insect Pitfall and the component variables of the AHI index, as well as the AHI index, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01). (See appendix for regression tables). 86 a) b)
c) d)
e) f) !
! !
g)
Figure 16. Comparison of avian call number and the component variables of the AHI index, as well as the AHI index, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. (* p ! 0.05, ** p ! 0.01). (See appendix for regression tables). 87
Sampling Radius of Acoustic Sensor
Average recorded amplitude values of each frequency tone at each starting dB level were plotted against the logarithmic distance from the SongMeter. A linear regression line was then fitted to each series, and where strong correlations were found, this line was used to estimate the radius of detection (Figure 17). The lower bound of the y-axis was automatically set based on the level of detection for the particular frequency at: 40 dB for 10,000 Hz and 5,000 Hz, 60 dB for 2,000 Hz, 75 dB for 1,000 Hz, and 50 dB for white noise. The level at which the regression line crossed the x-axis was then used to estimate the effective sampling radius of the SongMeter for each particular frequency amplitude combination. In several cases, only one point was detectable for a particular amplitude frequency combination. In such cases, the resulting regression line is very clearly not a good representation of the detection radius. Thus, the radius is assumed to be between the point detected and the next 10 m sampling point. The resulting values are thus a conservative estimate of sampling radius, based on the conditions during testing.
88 a) 10,000 Hz b) 5,000 Hz
c) 2,000 Hz d) 1,000 Hz
e) white noise
Figure 17. Sampling radius of the SongMeter (SM1) as determined using multiple frequency tones played at a six constant amplitudes, at increasing distances from the sensor. Linear regression fit lines are used to estimate the distance beyond which tones are not detected: a) 10,000 Hz tone, b) 5,000 Hz tone, c) 2,000 Hz tone, d) 1,000 Hz tone, e) white noise.
89 These results indicate that: detection of 10,000 Hz tones range from 35 m* to 400 m; 5,000 Hz tones range from 15 m* to over 1,000 m; 2,000 Hz tones range from 15 m* to 500 m; detection of 1,000 Hz tones range from 15 m* to 90 m; and detection of white noise ranges from 15 m* to 1,000 m. (Starred distances are estimated from distance of last detection, not the linear regression model).
From the first recorded data point at 10 m, one can also create graphs of the theoretical attenuation of the tone for each frequency-decibel combination, by subtracting
6 dB for each doubling of distance. By plotting the theoretical attenuation with the observed attenuation, one can see how excess attenuation affects each frequency in the tested environment (Figure 18).
90 a) 10,000 Hz b) 5,000 Hz
c) 2,000 Hz d) 1,000 Hz
e) white noise
Figure 18. Observed sampling radius of the SongMeter (SM1) as determined using multiple frequency tones played at a six constant amplitudes, at increasing distances from the sensor, showing excess attenuation. Logarithmic lines display theoretical attenuation: a) 10,000 Hz tone, b) 5,000 Hz tone, c) 2,000 Hz tone, d) 1,000 Hz tone, e) white noise.
91
This data clearly shows that, in the environment tested, excess attenuation affects the highest tone tested (10,000 Hz), and the lowest tone tested (1,000 Hz) to a greater extent than the two intermediate tones (5,000 Hz and 2,000 Hz) and white noise. This mirrors the pattern of estimated sampling radius seen above.
2.5 Discussion
The results of these studies indicate that the Modified Acoustic Entropy Index
(H’) shows emergent properties not seen in either of its component equations, Temporal
Entropy (Ht’) and Spectral Entropy (Hf’). As a whole, the H’ showed no significant relationship with the AHI or any of its component variables in 2009. In 2010, H’ was significantly related to biodiversity, as determined by plant heterogeneity (BD0 and
BD3). In the combined data from 2009 and 2010, H’ was again significantly related to blant heterogeneity (BD0). The 2010 and overall average data suggests that when representing a large number of seasons, H’ may be mediated by and representative of large-scale plant heterogeneity surrounding the recorder. Furthermore, because of the sensitivity of the H’ index to subtle changes in the soundscape, it is possible that H’ may be significantly related to the other variables tested only at certain times of the year or day. This hypothesis will be explored further in chapter three.
Significant relationships to the variables tested were also found in the traditional diversity assessment methods. Plant transect was significantly related to the
Agroecosystem Health Index (AHI0, AHI3), but interestingly not biodiversity as
92 determined by plant heterogeneity (BD) at either extent. This suggests that the plant transect method may not be representative of large-scale plant heterogeneity (in time and space), and thus may only be representative of very small-scale variation in plant life, which cannot be extrapolated to a larger scale. The significant relationship with the
Agricultural Health Index (AHI0 and AHI3), on the other hand, suggests that these small- scale variations may be relevant in some way to overall agroecosystem health. Insect pitfall was significantly related to soil quality (SOIL0, and SOIL3). This relationship is expected, as the pitfall method measures the diversity of insects that dwell in or on soil, and are likely affected by soil quality. Avian call number was significantly related to
Farm economics, which is simply the current agricultural use of the parcel (CAUV3).
This relationship, too, is easily explainable. The Avian population is highly determined by the use of the land being tested, and the use of the land that surrounds it.
Interestingly, none of the traditional methods of assessment were significantly related to each other, plant heterogeneity (BD) (the biodiversity measure used in the AHI study), or the H’ Index, thus calling into question whether any of the traditional methods is a good indicator of total biodiversity. The different pictures of biodiversity presented by these assessment methods are likely due in large part to the different biota sampled by each method, and how the sampled biota are representative of larger scale biodiversity.
Plants, for instance, are a section of and supporting element to larger scale diversity.
Plant diversity on a small scale, however, may have more to do with the particular type of habitat (in this case agricultural borders), and geographical location (in this case a several kilometer area in Wayne Co. Ohio) than with total diversity of the area. This can be seen in the closely matched plant diversity levels (as determined using the plant transect study)
93 seen in five of the six sites examined. The extremely low plant diversity level seen in
OH24 had more to do with recent mowing of the land adjacent to the crop and the fact that it was the one site not on a tree line, than with overall differences in diversity between it and the other sites examined. Thus, these differences did not comparably lower the diversity levels of ground-dwelling insect (whose population was related to soil quality) or vocalizing bird diversity (which was related to land use). Similarly, particular groups of ground dwelling insects are common in agricultural edge lands in Ohio, which generally have similar types of soils. While the diversity of these species was significantly related to soil quality, there was not as much of variation between sites as was originally anticipated (range = 0.5451), thus these groups were fairly equally represented in all sites tested in this study. In both cases, these methods would be more useful in calculating alpha diversity within a site after a large change in land use, or beta diversity levels between different habitat types, or similar habitat types under extremely different management systems, than for comparing similar habitat types under relatively similar and consistent management in close proximity to each other as examined in this study. Comparison of equal numbers of Amish or organically managed farms and conventionally managed farms would likely provide more divergent results.
Finally, the number of bird calls, most closely mirrored the patterns seen in the H’ index. This could be expected given the use of recorded sound in both methods.
However, a flock of birds passing over the site or several very vocal sedentary birds (both of which were present during testing in OH20) can so heavily skew data as to totally obscure general patterns in alpha and beta diversity. It is thus likely that this method, using this level of replication, does not have great enough statistical power to ensure that
94 statistical differences between sites will be captured where present. Increasing the number of replicates or the frequency of retesting could ameliorate this effect. The time and expense of such replication, however, can quickly become prohibitive. Statistical power will be discussed further in chapter three.
While the traditional methods sample only plants, ground dwelling insects, and vocalizing birds, respectively, the H’ index potentially represents all sound producing insects, birds, amphibians, and mammals. Because this group of biota represents a larger subsection of biodiversity, it may be more representative of total biodiversity levels, as can be seen in the index’s significant relationship to large scale plant heterogeneity, which was not seen in any of the traditional assessment methods. Furthermore, the inclusion of more genera makes skewing of the index by one group of species less likely.
Finally, the enhanced ability of the H’ index to detect subtle differences makes its use in assessing beta diversity between sites located in close proximity to each other, and changes within a site over time more practical. More testing of the H’ index, in comparison with total biodiversity inventories, in distinctly different sites, however, is needed to ensure that the index is indeed capturing differences in biodiversity and not simply differences in the behavior of sound producing biota between sites.
In addition to sampling a greater number of species, the H’ index also samples a greater area than the traditional assessment methods examined above. Characterizing the sampling radius of the H’ index, however, is far more complex than for the other assessment methods examined. Of all the methods tested, the sampling area of the plant transect method is the most well defined. Because plants are not mobile, the sampling radius is simply the length of the line used to form the transect. Furthermore, barring
95 human error, every plant along that transect will be represented in the diversity calculation. The sampling radius of the pitfall trap method is slightly more complicated and is dependent on the placement of the traps, the mobility of the insects in the area, and the probability of each individual insect being trapped in a pitfall. Calculation of the approximate sampling radius, however, is still rather straightforward and well established in the literature. Finally, the sampling radius of the avian call study is complicated by the great mobility of birds, and the sensitivity of the recording device used. Bird calls, however, occur within a relatively limited frequency and amplitude range. Using this information, calculating the sampling radius for stationary birds is rather straightforward.
Calculation of the sampling radius for the H’ index, on the other hand, is slightly more complicated because of the greater number of genera represented. Furthermore, the sampling radius for each group will vary based on the frequency and amplitude of the vocalizations of each group. Calculation of the relative sampling radius for each group, however, is possible given prior knowledge of the frequency and amplitude characteristics of organisms in the sampling area. The results of the radius study demonstrate the well-documented effects of excess attenuation of sound described above.
Overall, higher frequency tones, which compete less with environmental and anthropogenic noise (Forrest 1994), can be detected at greater distances, which explains why detection with the SongMeter generally decreased with frequency. Higher frequency sounds, however, experience greater absorption and thus are expected to be more difficult to detect at greater distances (Forrest 1994), which likely explains why 5,000 Hz tones were detected at greater distances than 10,000 Hz tunes. When viewed together, these factors explain the pattern of detection seen in this study, where detection decreases in
96 this order 5,000 Hz > 10,000 Hz > 2,000 Hz > 1,000 Hz. White noise’s detection level between 5,000 Hz and 10,000 Hz, can likely be attributed to the balance between high and low frequencies contained in the signal.
These graphs and knowledge of the frequency and amplitude of the target species’ vocalizations will allow researchers to determine effective sampling radius of the H’ in their study. It may, however, be valuable to replicate this experiment, in the environment to be studied and with the equipment to be used, prior to undertaking any H’ study, as factors affecting excess attenuation are dependent on the particular location. For the location and equipment currently tested, these results indicate that the sampling radius of the SongMeter is much greater than many traditional sampling methods, such as those examined in this study. Furthermore, the sampling radius of a study using the SongMeter can be greatly increased, with little extra investment of time and money, by placing several units in an array. Using the above data, and knowledge of the frequency and amplitude of the vocalizations of the organism being studied, researchers can avoid pseudo replication in recordings.
In addition to greater sampling areas, the H’ index also has a longer sampling period than the other assessment methods examined in this study. Because of the greater frequency of sampling, and greater length of data collection, it is possible to examine changes over time, including daily and seasonal patterns in the H’ data. Such data is crucial to our understanding of biodiversity. Using such rates we may be able to see cause and affect relationships between events and biodiversity levels, which cannot be seen as easily using traditional methods. For instance, in the 2009 data, it appears that the majority of vocalizations occur in the morning and evening, which mirrors the pattern of
97 temperature during the average day during the sampling period. Vocalizations also appear to intensify around sun up and just after sun down. Diel patterns will be further examined in Chapter 3.
The diel pattern of H’ is particularly evident in the 2009 data, for which I was able to use all three months of data collected for all sites. The inconsistency of the diel pattern in the H’ 2010 data is likely associated with the variable lengths of time used to calculate mean half-hourly H’. While three months of data was included in analysis for all sites in 2009, a much shorter period of useable data (slightly over one month) for two sites OH22 and OH23 was used in the 2010 calculations. A similar amount of data was used to calculate OH20 and OH21 in 2009 and 2010. Finally, a much longer five month period of collection was used to calculate mean half-hourly H’ of OH24 and OH25 in
2010. Therefore, the mean half hourly H’ data in 2010 actually represents three very different amounts of data. Thus, the 2010 data displayes far more variation between sites than is seen in 2009. It is likely that the one month of data used in the analysis of OH22 and OH23 did not provide the same analytic power as the data used in the other 4 sites in
2010, or the data used in 2009. Power analysis of the H’ index will be explored in chapter
3.
Further complicating matters, the 2009 data represents one seasonal transition, summer to fall, while the data from 2010 encompasses two seasonal transitions, spring to summer and summer to fall, for at least some sites. It is possible that there may be a very different pattern of diel H’ in each season, and that by averaging three of these patterns together, the overall pattern is lost. The larger pattern of pattern H’ in 2010, which includes multiple seasons, displays much less daily variation and appears to loosely
98 mirror the patter of daily temperature (Figure 2). Seasonal variation will be discussed further in Chapter 3.
The issues with the 2010 data highlight one of the biggest challenges with remote autonomous testing of any kind, sensor functional integrity (Hutto and Stutzman 2009;
Porter et al. 2005, Hutto and Stutzman 2009, Lammers et al. 2008). Such errors can be avoided, or their effects lessened, by frequent checking of the equipment. While the
SongMeters can run autonomously for up to three months, it is recommended that they be checked much more frequently. The consistent proper functioning of sampling units can lead to a false sense of security that must be avoided. While in many cases frequent checking of the equipment may be simple and cost effective, such methods may greatly limit the utility of autonomous sensing in more remote locations. For this reason, it would be highly beneficial to develop sensor units that are able to self-correct errors, or alternatively are able to notify researchers of errors (Porter et al. 2005). This could also be solved by wirelessly linking remote sensing units by an intranet to base stations, or by the internet to the researcher’s lab. Improvements in wireless internet technology could make this a distinct possibility in the very near future.
In summary, the results of this study suggest that the Modified Acoustic Entropy
Index (H’) may be as or more robust and responsive a measure of biodiversity as traditional methods of biodiversity, such as plant transect, insect pitfall, and avian point- count studies. The assessment of acoustic diversity using the H’ index, which I hypothesize to be a good surrogate for total biodiversity, was shown to be significantly related to plant heterogeneity (BD), the biodiversity measure used in the AHI study.
While the three traditional methods, also commonly assumed to be surrogates of total
99 biodiversity, were each significantly related to the AHI and/or one of its component variables, none were significantly related to plant heterogeneity. The H’ index may therefore be a better indicator of overall biodiversity in a site than the traditional assessment methods. Furthermore, the H’ index was able to detect these relationships on a much larger spatial and time scale, with a much lower investment of field research time and training than the traditional assessment methods. The consistency of the patterns seen and the agreement of the H’ index with plant heterogeneity (BD), index suggests that the results of this study support my thesis, that recorded sound may be used to detect significant differences in diversity, using acoustic diversity as a surrogate for total biodiversity, as or more consistently than traditional methods of biodiversity assessment.
100
Chapter 3: Temporal Parameters of the Modified Acoustic Entropy (H’) Index
3.1 Introduction
A key issue with many methods of assessing biodiversity is the time required to collect data. Tied to this is the impracticality of using most methods of biodiversity measurement frequently enough to identify temporal patterns, and the response of diversity levels to disturbance events. The ability of an assessment method to effectively collect data at spatial and temporal scales that make such analysis possible is critical for widespread and effective use (Duelli and Obrist 2003, Duelli 1997, Dobson 2005).
From the studies conducted in Wooster, Ohio (2009-2010), (Chapter two), a significant relationship of the acoustic entropy (H’) of sites with the biodiversity measure used in the Agroecosystem Health Index study, plant heterogeneity (BD), was found at least for some periods of time, whereas the more standard measures used to sample diversity over more brief intervals and smaller spatial scales each showed no significant relationship to plant heterogeneity (BD). While the H’ index values were not significantly correlated with traditional biodiversity indices (which also showed no significant correlation with each other), because it showed significant relationships with plant heterogeneity (BD), the H’ index is potentially more sensitive to differences in biodiversity than the traditional methods tested. Additionally, the H’ index can detect such differences with less labor and consequently less cost than other measures.
101 Furthermore, when the H’ index is calculated over longer periods, it can potentially be used to discern diel and seasonal patterns of animal diversity and/or activity, which cannot be detected as easily using more traditional methods. For example, all three traditional methods used above could be repeated to discern seasonal patterns and differences in diversity. Similarly, the insect pitfall and avian call methods could be used to examine diel patterns with increased repetition. Such repetition, however, requires a significant increase in labor, and thus will also cost significantly more. Furthermore, the increase in required field time may make such studies prohibitive in more remote or extreme locations.
To establish the efficacy of the H’ index in detecting such patterns, and the temporal and spatial scale of the method, I investigated the relationship between length of sampling period and statistical power of diversity comparisons, both diel and seasonal patterns of diversity measured by the acoustic sensors, and changes over time in acoustic measures of biodiversity after a period of anthropogenic sound.
3.2 Methods
3.2.1 Statistical Power of the Modified Acoustic Entropy Index (i.e. necessary length of sampling time to measure the variation present in sound generating biota).
Power analysis allows one to determine the sample size required for an investigation. In this case, power analysis is used to determine the necessary period of sampling for the H’ index. Generally speaking, power of analysis tests are used to determine the minimum number of replicates necessary to detect an effect of a given size.
102 Because what we are concerned with here is not just the number of replicates necessary
(sample size), but the length of acoustic sampling required to document which species are present (which may influence the precision of each mean), a somewhat non-standard method of calculation was used.
For each site, 13 weeks of data from 2009, and 22 weeks of data from 2010, was separated to produce average H’ values comprised of different numbers of weeks. Group one in 2009 was comprised of 13 one-week sets; group 2 was comprised of 6 two-week sets; group 3 was comprised of 4 three-week sets; and so on through group 13, which was comprised of one 13-week set. When the number of weeks remaining in a group was insufficient to make a set containing the desired number of weeks for that group, those weeks were excluded from further analysis of that group. For each group, the average mean H’ for each set was calculated using the methods described in chapter 2. Finally, standard linear regressions were then calculated to compare the average H’ to the AHI and its component variable for each set using SPSS (version 17). Groups in 2010 were similarly calculated. Because of the inconsistent sampling period for sites in 2010, however, all groups are not composed of equal numbers of sites. The one-week through five-week group averages are composed of six sites. The first four three-week blocks are composed of averages from sites OH24, OH25, OH20, and OH21. The final three three- week blocks and the four through 22-week blocks were all composed of averages from only OH24 and OH25.
To estimate how the length of the sampling period (number of weeks included in the mean H’) affects the average observed power, the standard errors from each regression were arranged for each group and plotted verses the number of weeks included
103 in sampling, for each set in each of the 13-week groupings in 2009 and 22 week groupings in 2010.
3.2.2 Diel and Seasonal Patterns
A key potential benefit of the H’ index, is its ability to detect rates of change in the acoustic entropy of a landscape, both natural and otherwise. Below we examine the ability of the H’ index to discern diel and seasonal patterns. To examine the differences among times of day (diel patterns) in acoustic entropy, mean hourly H’ values were calculated by averaging the values for samples on the hour and following half-hour.
These hourly values were then graphed to examine diel patterns. In order to determine how the H’ index is affected by different variables, at different times of the day, linear regressions were performed by hour of the day during 2009, 2010, and both years combined with the AHI and each of its component variables at a single pixel and three pixel extent. To examine the differences among times of the year, the one, two, three, and four-week groups calculated for the power analysis above, were graphed to examine seasonal patterns. In order to determine how the H’ index is affected by different variables, at different times of year, linear regressions were performed on each of these weekly groups for each time division in 2009 and 2010 with the AHI and each of its component variables.
104 3.2.3 Rebound Time of Soundscape After Extended Periods of Anthropogenic Acoustic
Disturbance
A unique feature of biodiversity in agricultural landscapes, such as those used in this study, is the coexistence of human and biological activity. The relationship between these two groups can have potentially positive or negative consequences that affect both communities. Of particular interest in acoustic studies, is the effect of anthropogenic sounds on the activity of the biological community. To investigate these effects, anthropogenic disturbances in the soundscape were identified by analyzing the Acoustic
Entropy values obtained from recordings from each site in 2009. To obtain these values, sound recordings were analyzed in Matlab ((version 7.7.0.471 (R2008b)) as described above, however, the 1 kHz filter was not used, in order to obtain as much information about the anthropogenic portion of the spectrum as possible. The resulting Acoustic
Entropy values were separated into 24 hour groups of 48 recordings each. These daily groups were then analyzed in SPSS (version 17.0) to identify outliers at a 95% confidence level, from interquartile ranges. Days showing more that one outlier were then chosen for further examination. The recordings identified as outliers, in the chosen days, were then analyzed by ear to determine the cause of the high Acoustic Entropy Value
(anthropogenic disturbance, wind, rain, electrical noise, issues with the sensor, etc). Days that contained two or more consecutive outliers caused by anthropogenic disturbance, typically caused by farm machinery, were then used for further analysis. For each day selected for further study, the period during which the acoustic disturbance occurred was identified by ear. The half-hour H’ values (filtered) for each day were then compared to the average half-hourly H’ values to determine the effect of anthropogenic disturbance on
105 the acoustic entropy of each site. Following the acoustic disturbance, the amount of time it took for H’ values to return to values above the half-hourly mean value was then examined. The average difference from the mean H’ values in the 24 hour period (0:00-
24:00) before, during and after the disturbance was calculated for each site. One-way
ANOVAs were then calculated for each site comparing: before and during, during and after, and before and after, to determine if statistical differences from the mean existed during any of these times.
3.3 Results
3.3.1 Power Analysis
Altering the number of weeks of sampling included in the mean H’ had a clear effect on the standard error of the slope in regressions of H’ with the seven independent variables. The standard error of the slope in 2009 showed a very consistent pattern for all variables. The standard error of the slope was relatively steady from one week to two weeks. The standard error of the slope increased slightly as the number of weeks of sampling included in the average H’ increased between two and three weeks, and then dropped sharply to its low at approximately five weeks. From this low the data increased sharply to its high at approximately eight weeks and decreased steadily to plateau near its initial value (Figure 19).
The standard error of the slope in 2010 showed a much less consistent pattern.
Generally, the standard error of the slope peaked distinctly three times at approximately five, 15, and 18 week groups, and had a distinct low point between 10 and 12 weeks.
After the final peak at 13 weeks, the standard error of the slope appears to begin to
106 decrease asymptotically toward zero as the number of weeks included in the mean H’ increases (Figure 20).
107 a) b)
c) d)
e) f)
g)
Figure 19. Relationship of the number of weeks of sampling included in mean H’ in 2009 to the component variables of the AHI index, as well as the AHI, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. 108 a) b)
c) d)
e) f)
g)
Figure 20. Relationship of the number of weeks of sampling included in mean H’ in 2010 to the component variables of the AHI index, as well as the AHI, at two scales (30 and 90 m2): a) BD, b) SOIL, c) ELV, d) MKT, e) SOC, f) CAUV, and g) AHI. 109
3.3.2 Diel and Seasonal Pattern Results
Comparison of the average hourly acoustic entropy values of test sites in 2009 revealed only two significant relationships between H’ and AHI or its components. Mean
H’ at 9:00 in 2009 was significantly related to plant heterogeneity (BD0: slope = 0.015,
SE 0.005, t = 2.902, p = 0.44; and BD3: slope = 0.016, SE 0.006, t = 2.786, p = 0.50)
(Figure 23) Many more significant relationships were found in 2010 (Figure 21). In 2010,
H’ was significantly related to: the Agroecosystem Health Index (AHI) at 3:00, 7:00, and
11:00; plant heterogeneity (BD0 and BD3) at every hour of the day except 10:00, 12:00,
18:00, and 19:00; soil quality (SOIL3) at 17:00; social organization (SOC3) at 19:00; topography (ELV3) at 15:00 and 21:00; and farm economics (CAUV3) at 3:00, 6:00, and
9:00. No significant relationships were seen in the hourly averages for 2009 and 2010 combined (Figure 22). See appendix for regression tables.
110 A B
Figure 21. Mean hourly H’ values for 2009. Letters indicate significant relationships (A = BD0: slope = 0.015, SE 0.005, t = 2.902, p = 0.44; B = BD3: slope = 0.016, SE 0.006, t = 2.786, p = 0.50).
111
Figure 22. Mean hourly H’ values for 2010. Letters indicate significant relationships.
112 Figure 23. Mean hourly H’ values for 2009 and 2010 combined.
Comparison of the average one-week acoustic entropy values showed a distinct seasonal pattern in both 2009 and 2010. Average weekly H’ values showed a bell curve which peaked in August, although the peak was several weeks earlier in 2010. Similar patterns were seen in the two week, three week, and four week averages. In 2009, mean weekly H’ was significantly related to soil quality (SOIL) in mid to late October (Figure
24). Similar relationships can be seen in the two week (Figure 25), the three week
(Figure 26) and four week (Figure 27) mean H’.
113 A B A B
Figure 24. Average weekly H’ in 2009 values with SD error bars. Significant differences indicated with letters (A = SOIL, B = SOIL 3).
114 A B
Figure 25. Average two week H’ in 2009 values with SD error bars. Significant differences indicated with letters (A = SOIL, B = SOIL 3).
115 A B
Figure 26. Average three week H’ in 2009 values with SD error bars. Significant differences indicated with letters (A = SOIL, B = SOIL 3).
116
B
Figure 27 Average four week H’ in 2009 values with SD error bars. Significant differences indicated with letters (B = SOIL 3).
In 2010, mean H’ was significantly related all tested independent variables, on at least one scale, during at least one period of time. Because we are concerned with mainly the biophysical variables, only the relationships of H’ to the Agroecosystem Health Index
(AHI0 and AHI3), plant heterogeneity (BD0 and BD3), and soil quality (SOIL0 and
SOIL3) will be discussed here. Full regression tables, which include all seven variables are available in the index. In all four groupings of weeks (1, 2, 3 and 4) H’ is consistently significantly related to the Agroecosystem Health Index (AHI0 and AHI3) during periods of low H’ proceeding periods of high acoustic entropy, which occurs mid July to mid
August (Figures 28, 29, 30 and 31). When broken into one week blocks, however, H’ was also significantly related to the Agricultural Health Index (AHI0 and AHI3) during the
117 peak in H’ at week 14 as well as the period of low H’ immediately following the period of high H’ (Figure 30). Similarly, in all four groupings of weeks (1, 2, 3 and 4) H’ is consistently significantly related to plant heterogeneity (BD0 and BD3) during periods of peak H’ (Figures 28, 29, 30, and 31). In the four week grouping, H’ is also significantly related to plant heterogeneity (BD3) in the period of low H’ prior to the increase in H’
(Figure 31). Finally, H’ is significantly related to foil quality in only two weekly groupings. In the two-week grouping, H’ is also significantly related to plant heterogeneity (BD0) during the period of peak H’ (Figure 29). In the four-week grouping,
H’ is significantly related to plant heterogeneity (BD0) only in the period of low H’ from late April to early May (Figure 31).
118
D D A B
B B A B A B B
Figure 28. Average one week H’ values for 2010 with SD error bars. Significant differences indicated with letters (A = AHI0, B = AHI3, C = BD0, D = BD3, E = SOIL0).
119 E C
B
Figure 29. Average two week H’ values for 2010 with SD error bars. Significant differences indicated with letters (B = AHI3, C = BD0, E = SOIL0). .
120
D
A B
Figure 30. Average three week H’ values for 2010 with SD error bars. Significant differences indicated with letters (A = AHI0, B = AHI3, D = BD3). .
121
B
A B E D
Figure 31. Average four week H’ values with SD error bars. Significant differences indicated with letters (A = AHI0, B = AHI3, D = BD3, E = SOIL3).
In both 2009 and 2010 the standard error of the mean was generally high during periods of high H’ and low during periods of low H’. In both years only one period of high H’ is captured, which occurred during August. In 2010, this increase in H’ occurred earlier and was slightly longer (mid September through mid August) than in 2009 (mid to late August). The peak value of H’, however, was similar in both years (2009 H’ =
0.0153; 2010 H’ = 0.0170). Additionally, both years displayed similarly low minimum weekly H’ values (2009 H’ = 0.0042; 2010 H’ = 0.0040). In both years, standard error becomes more smaller and less variable as more weeks are added to mean H’. This pattern is more evident in 2010. In 2009, however, the standard error of the mean H’ also
122 decreases as the season progresses. Thus, in 2009, standard error of mean H’ is much greater for the first four week block than the last.
3.3.3 Anthropogenic Disturbance
The methods described above were able to identify and characterize periods of anthropogenic disturbance in all six sites. Results of the analysis of rebound indicate that for all but two sites, the average difference in H’ between the average half-hourly mean and the half-hourly mean during a disturbance significantly differed from the same measure before and after the disturbance. The measure before and after, however, did not differ from each other (Figure 32). The two sites that did not show significant differences between the anthropogenic acoustic disturbance and the average half-hourly mean during the same period were OH24, the OSU sheep unit, and OH22, the Amish farm.
123
Figure 32. Difference between the average mean H’ and the average observed H’ during each period for each site. Significant differences indicated by stars. OH21 (df = 2, F = 5.075, p = 0.016), OH20 (df = 2, F = 9.066, p = 0.004), OH25 (df = 2, F = 13.987, p < 0.001), OH24 (df = 2, F = 2.005, p = 0.177), OH22 (df = 2, F = 6.000, p = 0.579), OH23 (df = 2, F = 6.557, p = 0.004).
3.4 Discussion
The results of the power analysis indicate that the precision of H’ and subsequently the power of the H’ index are related to the length of the sampling period used in calculations. Generally, as the number of weeks included in mean H’ increases, the standard error of mean H’ decreases. The additional variation seen in the standard error data are likely related to seasonal variation in H’ that is introduced when more weeks are included in the mean. In 2009, for instance, the decrease in the standard error 124 of mean H’ at five weeks coincides with the transition from summer to fall in early
September. Two weeks later, the mean standard error resumes its decreasing pattern.
Similarly, in 2010, the standard error of mean H’ shows a decrease in standard error of mean H’ at approximately three weeks, which corresponds with the transition from spring to the beginning of the growing season in early May. The effects of these seasonal transitions on standard error of mean H’ decrease as these seasonal variations are averaged out over time. Thus, sampling period of sufficient length to account for changes in season should be used to ensure adequate power of analysis.
The greater variation in the standard error of mean H’ data in 2010 can likely be attributed to several different factors. A good deal of this variation is likely caused by differences in the sampling periods between the two years. As a much longer sampling period was used in 2010 than in 2009, the 2010 data includes much more seasonal variation than in 2009. As was seen in 2009, such seasonal variation likely affects the standard error of the mean H’ beyond the variation caused by addition of weeks to the mean. Additionally, as mentioned above, mean H’ values in 2010 were not composed of equal numbers of sites for each weekly grouping. The inclusion of fewer sites in the calculation of mean H’ as the number of weeks included in mean H’ increases is likely to decrease the power of each of the later mean H’ values. Thus, as the number of sites included in the mean decreases, the standard error of mean H’ is much more likely to be affected by the variation within one site. This may explain the increase in standard error of mean H’ at approximately 15 weeks, when the number of sites included in the regression drops from four to two. An analysis of a similar time period, which included data from six or more sites would provide a much more useful analysis of the effect of
125 increasing the number of weeks included in the mean (beyond 13 as used in 2009) on the power of analysis.
Overall, the data from 2009 and 2010 suggest that for six test sites the most precise and reliable results will be observed after at least seven weeks of sampling. Using this sampling period should ensure adequate analytical power, and account for variations within sites caused by seasonal transitions. If one needs to calculate H’ index comparisons in less than seven weeks, more sites should be used to ensure adequate power. This relationship between the number of sites used and power of analysis is supported by the increased variation seen in the 2010 data as the number of sites included in mean H’ decreases. Importantly, however, these results indicate that comparisons could hypothetically be done in a very short period of time, given a large enough sample size. Alternatively, if fewer replicates are available, the sampling period should be increased to ensure adequate power. This too is supported by the 2010 data, which shows that beyond 18 weeks of sampling included in the mean, standard error again begins to reliably decrease with increased sampling time, even though only two sites are included in the analysis. Simple cost benefit analyses should be done to determine the best combination of sampling period, number of sensors, and number of sites for a given study. Shorter sampling times, however, may not account for seasonal transitions.
Furthermore, behavioral variation can exert a strong influence on acoustic diversity over very short time intervals. As noted in Chapter 2, a flock of birds passing over the site or several very vocal sedentary birds can so heavily skew data as to totally obscure general patterns in alpha and beta diversity when monitoring periods are too short. It is therefore
126 crucial that sampling periods be long enough to account for these variations within sites, even when replication is high.
Examination of the diel pattern from 2009 indicates that the only significant relationship between mean hourly H’ and the three biophysical variables from the AHI study occurs at 9:00, when mean H’ is significantly related to plant heterogeneity (BD0 and BD3). In 2010, however, mean hourly H’ is significantly related to the
Agroecosystem Health Index (AHI0) at 3:00, 7:00, and 11:00. Furthermore, in 2010 mean hourly H’ is significantly related to plant heterogeneity (BD and/or BD3) in nearly every hour of the day. This disparity in hourly detection, as well as total detection seen in chapter two, highlights the value of increasing the length of sample period, and sampling across seasons, in order to determine how the overall H’ pattern relates to diversity levels.
Further examination of the total average diel pattern in 2010, however, indicates that while increasing sampling period provides a more robust assessment of the overall diversity in a site, it can also average out seasonal behavioral patterns evident in shorter sampling blocks, as seen in 2009. Therefore, when examining diel patterns of behavior, it may be more useful to look at shorter sampling periods within a season or across only one seasonal transition. The value of such analysis can be seen in the diel pattern from the first year of this study. The mean hourly H’ data from 2009 shows that H’ reaches its lowest point at 19:00, just before sunset, then rapidly increases to its peak, between 21:30 and midnight, and then decreases at a more or less steady rate until it again reaches 19:00.
Furthermore, much more variation in average H’ between sites occurs during the middle of the day. The level of variation during the morning and evening, on the other hand, is comparatively quite low, indicating very consistent patterns of vocalization at these
127 times. The most ecologically significant patters may be in these consistent periods of high vocalization, during crepuscular periods. This observation is consistent with what is known about diel periodicity in animal sound.
The sharp increase in H’ encompassing sunset is likely due to the increase in calling behavior of birds (especially nocturnal species), insects, and anurans at this time, often referred to as the dusk or evening chorus. A large body of work has found that passerines experience as small secondary peak in calling intensity just after sunset
(Amrehein, Kunc, Naguib 2004; Hardouin Robert, Bretagholle 2008; Slagsvold 1996).
Furthermore, nocturnal species of bird, such as owls, call primarily during dusk and the early evening (Hardouin, Robert, Bretagholle 2008). Similarly, many species of insect chorus most intensely during the night. Katydids, for example, call from approximately
18:30 to 21:30 (Nityananda and Balakrishnan 2008; Robinson and Hall 2002). Many crickets call during both the day and night (Robinson and Hall 2002). Cicadas on the other hand, chorus most intensely around dusk and dawn (Sueur 2002).
Additionally, a large portion of the evening peak observed in the H’ data is the result of anurans. Breeding anuran species often chorus just after sun down, during their breeding season (Bridges and Dorgas 2000; Hsu, Kam, Fellers 2006; Stevens and
Paskowski 2004). Several studies, however, have found that the time of peak calling rates for species of anuran is often species dependent (Bridges and Dorcas 2000; Hsu, Kam,
Fellers 2006). For instance a 2000 study by Bridges and Dorcas of nine anuran species calling behavior, found that while most Hyla species displayed peak calling around
22:00, Rana species displays peaked much later, between 4:00 and 6:00. Similar results were found in a study of subtropical forest anurans (Hsu, Kam, Fellers 2006). These
128 results suggest that temporal partitioning of the soundscape, to avoid conspecific competition (Bridges and Dorcas 2000; Hsu, Kam, Fellers 2006). The presence of several anuran populations, therefore, could account for the variation and multiple peaks in H’ seen at several of the sites tested in this study.
A smaller secondary peak is seen in the H’ data around dawn. As many species of insect and anuran begin to decrease their calling behavior late in the night, their vocal activity is replaced in the soundscape by a large number of avian species. This increase in vocalization activity of male birds, during the breeding season, in the several hour period of time encompassing sun up, often called the “dawn chorus” (Amrehin, Kunc, and
Naguib 2004, Foote et al. 2008), likely accounts for this smaller early morning peak seen in the 2009 H’ data (Figure 21). Following this increased period of dawn chorusing, H’ decreases steadily until the evening chorus begins again.
Further in-depth examination of the mean hourly H’ data from 2009 and the audio recordings from these peak times would help to elucidate whether the differences in mean hourly H’ at different times of day are the result of sampling entirely different assemblages of biota at different times of day, or if the same assemblages are represented throughout the day but simply vocalize more intensely during certain time periods. It is likely, however, that a combination of these effects is taking place. Many groups of species, such as those mentioned above, can be heard throughout the day, but vocalize most intensely and reliably during certain times of the day. It is also likely, however, that some groups may only vocalize at significant levels during certain times of the day. To assure without a doubt that all vocalizing biota in range of the sensor are included in mean H’ it is necessary to include all hours of the day in analysis.
129 Depending on the specific research question being examined, however, sampling only during these peak periods around dusk and dawn, or throughout the night, may provide enough data to compare biodiversity between sites. This is especially true of questions regarding a certain groups of biota known to vocalize most reliably and consistently at these times of day. Restricting sampling to fewer periods of time per day could allow sensors to be left in the field for longer periods of time, assuming the issues with reliability mentioned above have been addressed, by decreasing the power and storage used per day. It is important, however, that sampling periods used encompass enough time before and after sun up to account for differences in the start and end time of the dawn chorus associated with seasonal changes, weather patterns, etc (Amrehin, Kunc, and Naguib 2004).
Examination of the seasonal pattern in both 2009 and 2010 shows that mean H’ increases to a peak high in late August. This peak in acoustic entropy may correspond with seasonal patterns of breeding. It is difficult, however, to make many concrete conclusions based on the seasonal patterns recorded here, because of the relatively short period of time represented by this study. Furthermore, the sampling period for 2009
(August – early October) does not encompass the full breeding season of most of the represented biota. While the longer sampling period used in 2010 (late April to early
October in 2010) encompasses the full breeding cycle of many biota, the lower replication in the later months of the study makes this data somewhat less clear and reliable. Given an entire year of H’ data for at least six sites, however, a more complete seasonal cycle of territory establishment, mate selection, breeding, brooding, etc would likely be discernable in this type of weekly analysis. From the available data, however, it
130 is clear that a great deal of vocal activity occurs in early August, which likely corresponds with some aspect of breeding cycles.
Examination of the seasonal regression data from 2009 and 2010 reveals further information about what may be driving H’ levels throughout the year. Furthermore, the changing relationships between the tested variables and H’ highlight the importance of examining H’ data on multiple time scales, to fully explore the effects of independent variable, which may be acting on different time scales. Although the combined average of H’ from 2009 showed no significant relationship with any of the independent variables tested, a significant relationship with soil quality (SOIL0 and/or SOIL3) was consistently found in the weekly grouping data. This relationship, was found after mean H’ began to decrease, in late September and early October. This pattern suggests that acoustic entropy in the fall is driven by the vocalizations of soil dwelling biota. This hypothesis is supported by the significant relationship between the Insect pitfall data and soil quality
(SOIL 0 and SOIL3) (Chapter two).
A slightly different pattern of relationships is observable in the 2010 data.
Because of the difference in sampling period the weeks in 2009 when a significant relationship was found between mean H’ and soil quality were not captured in 2010. A significant relationship between mean H’ and soil quality (SOIL0) in 2010, however, was observed in the spring (4/24-5/21), a period which was not captured in 2009. This could indicate increased numbers of vocalizing ground dwelling insects at this time, or could be associated with soil quality’s effects on plant growth. The most noticeable pattern of relationship seen in 2010 mean H’, however, is the consistent association of plant heterogeneity, the biodiversity measure used in the Agroecosystem Health Index, with
131 the periods of highest mean H’. Furthermore, a significant relationship between soil quality (SOIL0) and mean H’ is also observed during this peak period, in the two week grouping. When considered together these results suggest that periods of high mean H’ are strongly associated with large-scale biodiversity of multiple groups of biota. A similarly consistent significant relationship between mean H’ and the Agroecosystem
Health Index (AHI0 and AHI3) is observed in the period just before the increase in mean
H’, in 2010. This may indicate that during periods of non-peak activity, acoustic entropy is driven by complex variation in overall agroecosystem health, rather than large-scale biodiversity or the increased activities of one or a few groups of organisms.
While analysis of H’ data may elucidate many patterns of diversity associated with the time of year, and differences between sites, it is crucial that inferences not be made from such data without first carefully considering whether observed changes in H’ are the result of actual changes in biodiversity, or rather changes in the behavior of the recorded biota. For instance, many species change their vocalization patterns based on the season. Increased calling behavior associated with breeding season, as seen in many genera including birds, anurans, and insects (Amrehin, Kunc, and Naguib 2004; Avery,
Quince, and Sturdy 2008; Foote et al. 2008; Robertson, Fontaine, and Lomis 2009; Selmi and Boulinier 2003; Tremaine, Seiston, and Mennill 2008; and Wilson and Bart 1985), in particular, will produce relatively high H’ values during this part of the year. Decreased
H’ values outside of the breeding season, therefore, may not necessarily indicate decreased diversity.
The decrease in H’ could be explained in several ways other than diversity decreases. For example, many species of bird, including pileated woodpeckers
132 (Tremaine, Seiston, and Mennill 2008), decrease the frequency and intensity of their vocalization activity following the breeding season, but remain active in their territory.
Similarly, many species of birds, including black capped chickadees (Avery, Quince, and
Sturdy 2008), drastically change the characteristics of their song based on the season, throughout the year. Both of these changes in vocalization would likely alter the characteristics of the soundscape, and could thus alter H’ values independently of changes in diversity. Alternately, some species, including many insects (Sueur and
Sanborn 2003) and amphibians (Cunnington and Fahrig 2010), have both vocalizing and non-vocalizing life stages. Thus, H’ values will be higher during the spring and summer months when the vocalizing stages are more prevalent and lower during the fall and winter months when the non-vocalizing stages are present. This change, however, also does not necessarily indicate a change in overall diversity. Similar effects will be seen with hibernating species. Finally, migrating species will likely cause H’ values to decrease when they vacate the habitat, and while this decrease does represent a temporary change in diversity, it is likely not a permanent decrease in diversity and does not necessarily signal anything about the health of the ecosystem. Similar effects to these may be seen on a smaller scale in diel patterns of activity. Careful examination of the H’ data in combination with associated data such as temperature, time of day, time of year, etc will help to separate these differences in H’ from those that indicate an ecologically relevant changes in diversity that are unrelated to diel or seasonal patterns. Furthermore, for comparison between sites, or of the same site between years, utilizing a sampling period of adequate length and thus adequate power will help to average out the variability in the H’ index associated with time period.
133 Examination of the disturbance data indicates that extended periods of anthropogenic acoustic disturbance do not have a lasting affect on the represented biota beyond the disturbance period. A rebound period of acoustic activity may take place in less than 30 minutes, and may therefore take place between sampling periods, and thus would not be captured using this methodology. Further examination of anthropogenic disturbances, using more frequent sampling, is needed to determine if biologically relevant differences in rebound time exist between sites. Given the current data, however, it seems unlikely that biotic communities are responding differently to anthropogenic disturbances in high and low AHI sites.
One possible reason for the lack prolonged response to anthropogenic disturbance could be that agroecosystems are so frequently disturbed by human activity that the biotic communities that live there have become adapted, or at least accustomed, to high levels of anthropogenic disturbance. For example studies of both birds (Brumm 2006) and amphibians (Cunnington and Fahrig 2010) have shown that individuals in areas with high traffic noise adjust their vocalization to avoid signal masking, most often increasing their frequency and decreasing the length of their calls, in comparison to nearby populations of the same species in more quiet habitats. As a result, these individuals do no experience call masking and thus do not experience a decrease in fitness from increased anthropogenic sound alone. A study of anuran response to traffic noise found that play back of a recording of traffic sound to a rural population unaccustomed to such noise, resulted in changes in vocalization characteristics similar to those seen in more urban populations which routinely experience frequent road noise, suggesting that vocalization plasticity may be an adaptive trait found in certain species (Cunnington and Fahrig 2010).
134 Biota that are particularly sensitive to such disturbances may be entirely absent from these habitats.
Comparisons of the response of biota to anthropogenic disturbances using this methodology in agroecosystems and more pristine habitats are needed to determine if this is the case. Such impacts have been observed in the negative reaction of sea life to the noise from motorboats (Lammers et al. 2008). Similarly, these methods could likely be used to examine changes in alpha diversity in habitats with greater anthropogenic disturbances, or habitats undergoing transitions in use, as was seen in the Cai et al. (2007) study of Lewis rails near airport construction. Here too, however, more research is needed to ensure that the sampling schedule used will be able to capture changes in diversity and/or activity in response to anthropogenic disturbances.
Interestingly, the two sites that did not show significant differences between the anthropogenic acoustic disturbance and the average half-hourly mean during the same period were OH24, the OSU sheep unit, and OH22, the Amish farm. The reasoning for these similar results may be quite different, in each site. Anecdotally, the Sheep Unit was the most disturbed site. The lane between the corn plots was regularly mowed, which was not common on the other farms, and the area of the property experienced a much higher level of human traffic than the other sites. Thus, it seems likely that particularly sensitive species are not present in this location, and that the biota present in this site are particularly accustomed to disturbance, although this cannot be proven without further testing. In contrast, the Amish farm was most likely the least disturbed site. There is much less human traffic at this site, than the others, and no machinery or inorganic amendments are used there, ever. It seems likely that the lack of significant difference
135 between the average H’ and the H’ during the disturbance at this site, may be because the noise produced by the horse drawn equipment was not disturbing to the robust population of biota at this site. This hypothesis, however, also needs to be examined further.
136
Chapter 4: Summary and Conclusions
4.1 Overview of Study
Biodiversity loss is occurring globally at a staggering rate (Ulgiati and Brown
1998; United Nations 1992). Difficulty assessing biodiversity levels in a timely fashion, however, makes it challenging for scientists to determine where and to what extent these losses are occurring. In order to mitigate the negative effects of human actions on biodiversity, a robust, responsive, and reliable measure must be developed that will give scientists and the public an accurate picture of how our actions affect biodiversity, and how biodiversity levels affect us (Dobson 2005; Duelli 1997; Kremen and Ostfeld 2005;
Spangenberg 2007). This study explores a potentially more robust, responsive and reliable biodiversity measure.
This study was conducted in agroecosystems in North East Ohio. Biodiversity in an agricultural context is of particular interest because agroecosystems represent a unique intersection between human society and the environment. Agroecosystems are among the ecosystems most affected by human activity and are one of the ecosystems that could most benefit from increases in biodiversity. Within agricultural systems, biodiversity is critical for the maintenance of primary production, soil formation, nutrient cycling, biological control of pests, and many other factors on which food production is dependent, and must therefore be monitored and preserved (Altieri 1999; Altieri and
Rosset 1996; Swift, Izac, van Noordwijk 2004). The Conference of Parties (COP),
137 established by the UN Convention on Biodiversity, has recognized that “the special nature of agricultural biodiversity, its distinctive features, and problems need distinctive solutions” (United Nations 1992).
The Modified Acoustic Entropy Index (H’), developed for this study, attempts to remotely assess biodiversity levels by examining how complete the acoustic soundscape is, which would relate to how many different sounds and sound patterns are occurring, and compares the soundscape patterns of different locations. This method can be used to monitor both ! and " diversity on all three relative scales: (1) an ecosystem’s contribution to larger scale diversity, (2) comparisons in space, and (3) comparisons in time (Duelli 1997). Additionally, by not focusing on a specific species or genera of biota, this technique examines a more ecologically relevant taxonomic range than many methods that look at only one group of organisms (Dobson 2005). Although the use of sound to estimate biodiversity does not account for plant, microorganisms, or other silent species, it does provide a comparative indicator of the relationship between different groups as well as the impact of events on ecological communities (Lammers et al. 2008;
Sueur et al. 2008).
The H’ index was developed to address the challenges faced by biodiversity assessment by being reliable, repeatable, and financially reasonable (Dobson 2005; Duelli
1997; Duelli and Obrist 2003). The H’ index also effectively minimizes many biases associated with biodiversity assessment by automating much of the collection and analysis procedures. By removing the human observer from the field, the H’ index effectively eliminates error that could be caused by inter-observer error and the observer’s presence in the environment (Celis-Murillo, Deppe, Allen 2009; Hutto and
138 Stutzman 2009; Porter et al. 2005). Additionally, the H’ index avoids sampling error by standardizing sampling effort. The sensor units used in this study are relatively inexpensive and can be set up quickly and easily by technicians with minimal training.
Thus, such systems provide the technology to increase the spatial and temporal scale of monitoring while reducing human labor and error (Celis-Murillo, Deppe, Allen 2009; G.
Sanchez, R.C. Maher, and S. Gage).
This study examined the development of the H’ index. To do this, the H’ index’s linear relationship with seven independent variables associated with agroecosystem health was compared with three traditional methods of biodiversity estimation.
Additionally, the spatial scale (sampling radius) and power (necessary length of sampling) of the H’ index were determined. The ability of the H’ index to discern diel and seasonal patterns was also examined. Finally, the ability of the H’ index to detect the response of the biotic community to anthropogenic acoustic disturbances was assessed.
4.2 Key Findings
• The modified acoustic entropy index (H’) was able to consistently detect significant
relationships between acoustic diversity measured over several months and plant
heterogeneity (BD), the measure of biodiversity used in the Agricultural Health
Index. In shorter time periods, the H’ index was consistently related to three known
indicators of agroecosystem health (AHI, BD, SOIL). The traditional assessment
methods examined, however, were never significantly related to plant heterogeneity
(BD) and were never significantly related to more than one indicator of
agroecosystem health (Chapters 2 and 3). The lack of a significant relationship of any
139 of the traditional assessment methods and plant heterogeneity, as well as the lack of
correlation between any of the traditional assessment methods calls into question the
strength of any of these methods as a surrogate for total biodiversity. While the H’
index was not significantly correlated with any of these traditional assessment
methods either, the H’ index was significantly related to plant heterogeneity (BD), as
well as soil quality (SOIL) and the Agroecosystem Health Index (AHI). Furthermore,
the H’ index was able to elucidate consistent seasonal and diel patterns. When viewed
as a whole this suggests that the results of this study support my thesis, that recorded
sound may be used to detect significant differences in diversity, using acoustic
diversity as a surrogate for total biodiversity, as or more consistently than traditional
methods of biodiversity assessment. More testing of the H’ index, in comparison with
total biodiversity inventories, however, is needed to ensure that the index is indeed
capturing differences in biodiversity and not simply differences in the behavior of
sound producing biota between sites.
• Additionally, the spatial scale of the H’ index, as calculated using the SM1
SongMeter, was much greater than any of the traditional methods tested, even for low
amplitude sounds. Detection ranged from 15 m for low amplitude tones (40 dB) to
over 1,000 m (for 5,000 Hz tones over 60 dB). While the maximum distance of
detection varies with the frequency and amplitude of sounds, the laws of sound
attenuation and excess attenuation make the sampling radius for sounds with known
characteristics relatively predictable.
• Power of analysis of the H’ index showed that a relatively long sampling period
(greater than 7 weeks) may be necessary to consistently determine differences in
140 acoustic entropy between sites (beta diversity). Increasing the number of replicates,
however, should decrease the necessary sampling period.
• Furthermore, the long sampling period used in calculation of the H’ index was able to
be divided into groups that allowed the detection of consistent diel and seasonal
patterns within sites (alpha diversity), and to detect significant relationships to a
greater number of know indicators of agroecosystem health (plant heterogeneity, soil
quality, and the Agroecosystem Health Index) (beta diversity), which may be related
to season, than were detected in the shorter sampling periods used to calculate the
traditional biodiversity assessment methods.
• While it was possible to use the H index to detect anthropogenic acoustic
disturbances, and the H’ index was able to detect decreases in biotic vocalization
during these disturbances, the rebound of the biotic community was not seen using
this methodology. Furthermore, the H’ index was unable to detect a threshold level of
anthropogenic sound, beyond which biotic sound significantly decreases, in the
agroecosystems examined in this study. Longer samples or more frequent sampling is
needed to determine if such a threshold exists in the agroecosystems tested. Further
replication of the method in more diverse agroecosystems, or alternatively less
frequently disturbed habitats, is also needed to determine the appropriate length and
frequency of sampling needed to determine the response of biological communities to
anthropogenic disturbance. Required length and frequency of sampling may differ by
habitat type.
• Finally, the H’ index is simpler and required less field time, training, and expense to
conduct than many, if not most, traditional biodiversity assessment methods. In
141 addition, because recording devices can be left to collect data in the field for a very
minimal additional cost, data can easily be continuously collected over many weeks,
months or even years. The resulting H’ index therefore provides a long-term record of
diversity fluctuations, whereas many traditional methods provide only a snap shot of
diversity at the time of the assessment. This type of continuous data can elucidate
patterns and rates of change in diversity that would be extremely difficult to detect
with traditional methods.
4.3 Policy Recommendations and Future Research
Applications of remote assessment technology, such as that used in this study, can provide data in scales and time frames that were previously not practical. Such data may reveal previously unobservable phenomena. This technology has the potential to significantly extend research capabilities by making feasible intense sampling on a larger scale, with higher frequency observations, than would otherwise be practical.
Furthermore, this remote technology allows researchers to make observations unobtrusively, in extreme conditions, in real (or near real) time, and thus connect the field with the lab by effectively decreasing the distance between researchers and their field sites (Blumstein et al. 2011; Kasten, McKinley, and Gage 2007). Such data streams will increase what is possible and practical in terms of addressing the “grand challenges of environmental science” (Porter 2005).
In the future, this technology could be extended to entire watersheds, foodsheds, or regions, to more or less continually monitor these ecosystems. Data from such studies could provide local and regional policy makers with a useful tool to interpret the effects
142 of human activity on biological communities, to monitor the state and functioning of biodiversity (and thus related ecosystem services) in agroecosystems, and to quickly detect changes in ecosystem health (Dudley et al. 2005; Kasten, McKinley, and Gage
2007). This type of work is already being explored by several groups, including those at the Remote Environmental Assessment Laboratory (REAL), who are working to set up acoustic sensor networks in various locations around the world (Gage 2008, REAL).
Such studies would be particularly useful in sensitive ecosystems that are undergoing a change in use or management. While the H’ index could be used to detect negative changes in diversity, it could just as easily be used to support the efficacy of remediation methods.
4.4 Limitations and Future Research
As with many studies of biodiversity assessment methods, the results of this study raise as many questions as they provide answers. None of the traditional methods of biodiversity assessment examined in this study were correlated with each other or with
H’. Each measure was, however, significantly related to one of the independent variables tested here. If these measures were indeed good surrogates for total biodiversity, one would still expect them to be correlated with each other. These results call into question whether any of these commonly used methods provide a useful estimation of total biodiversity, even when their biases are taken into account.
While the H’ index was not correlated with any of the traditional methods tested either, the consistent patterns observed in the H’ index and the greater portion of biota included in the metric, as well as its significant relationship with all of the independent
143 variables that were significantly related to the traditional methods (AHI, BD, SOIL) suggest that the H’ index may be a better representative of ecosystem biodiversity than many traditional assessment methods, including those examined in this study. More tests that compare H’ index values with frequently repeated total biodiversity inventories, however, would be useful in determining the extent to which the H’ index is actually representative of total biodiversity on various temporal and spatial scales. Such studies will help to determine whether H’ is a correlate, or merely a surrogate of total biodiversity. The consistency of the preliminary results from this study, however, suggest that the H’ index is at the very least a better metric of total biodiversity than the type of traditional assessment methods tested here.
Repetition of these methods in more agroecosystems, especially systems which have more divergent management methods, those in different parts of the country or world, and those undergoing transition would help to establish the usefulness of the H’ index in monitoring biodiversity in agroecosystems. Repetition of these methods in other types of ecosystems would be useful to establish usefulness of the H’ index in monitoring diversity levels and rates of change in general. Furthermore, the H’ index needs to be tested in less frequently, or more recently, disturbed habitats to determine if it is useful for detecting rebound rates and/or the extent of a disturbance’s effect on the biotic community.
Currently, the greatest limitation to the widespread use of the H’ index is technology. Technology at the sensor level leaves the most to be desired. While current sensor technology, such as that used in this study, is adequate, it has still not reached a stage of reliability that would make its use in truly remote locations practical. As it
144 stands, issues with sensor power, data storage, and error must still be addressed. While sensors are rapidly becoming smaller and more energy efficient, they still require a sustained power source. The SongMeters used in this study run on four D-cell batteries, and at the sampling cycle used here, can hypothetically run autonomously for up to three months. After this period, however, the batteries must be replaced. Sensors that are able to run on less power for more time would greatly improve the extensibility of these studies. Use of alternative sources of energy (i.e. solar and wind) combined with rechargeable batteries could be of use in this respect. Such solutions, however, will not be practical in all locations. Thus, improvements in power storage/use will likely be necessary for some habitat types.
In addition to power usage, data storage is a significant limiter of the remote use of sensor technology. With the sampling cycle used in this study, the SongMeter recorded a 30 second, 1.9 MB wav file every 30 minutes. Thus a gigabyte of data (~539 files) is reached in just under 18 days. While SD storage cards can be purchased at a reasonably low price that accommodate several months of data, the number and size of files becomes unwieldy rather quickly. The simplest solution would be to wirelessly link sensors to a larger server that could store files remotely. Another, perhaps more elegant option that could be combined with wireless technology, would be to develop computer controls for sensors that were able to autonomously analyze sound data and then store or transmit only the processed data, such as the H’ value.
Perhaps the most serious issue impeding the widespread used of remote sensors is sensor failure. Such repeated and widespread sensor failure was encountered in this study, that over four months of data from 2010 was unusable in analysis. Failure of one
145 sensor can make not only the data from that location unusable, but can also make data from comparison sites unusable. In the short-term, sensors should be checked frequently, perhaps weekly, to ensure continuity of data. In the long-term, to remedy this problem, machines simply need to be designed to be more sturdy and reliable. In addition to this, sensor units that could correct errors themselves, or at the very least notify researchers when they are not working properly would be an invaluable improvement. Again, wirelessly connecting sensors to a central location would improve function, in this case by making monitoring of function easier. Fortunately, widespread improvements in all these areas and significant decreases in size and cost are steadily making true widespread remote use of this type of sensor more and more practical.
146
References
Acevedo MA and Villanueva-Rivera LJ. 2006. Using automated digital recording systems as effective tools for the monitoring of birds and amphibians. Wildl Soc Bull 34(1):211-4.
Alkorta I, Albizu I, Garbisu C. 2003. Biodiversity and agroecosystems. Biodivers Conserv 12(12):2521-2.
Allen-Wardell G, Bernhardt P, Bitner R, Burquez A, Buchmann S, Cane J, Cox PA, Dalton V, Feinsinger P, Ingram M, et al. 1998. The potential consequences of pollinator declines on the conservation of biodiversity and stability of food crop yields. Conserv Biol 12(1):8-17.
Altieri MA. 2004. Linking ecologists and traditional farmers in the search for sustainable agriculture. Front Ecol Environ 2(1):35-42.
Altieri MA. 1999. The ecological role of biodiversity in agroecosystems. Agric Ecosyst Environ 74(1-3):19-31.
Altieri MA and Rosset P. 1996. Agroecology and the conversion of large-scale conventional systems to sustainable management. Int J Environ Stud 50(3):165.
Amrhein V, Kunc HP, Naguib M. 2004. Seasonal patterns of singing activity vary with time of day in the nightingale (luscinia megarhynchos). Auk 121(1):110-7.
Amphibian Research and Monitoring Initiative concepts and implementation [Internet]. Reston, Va.: U.S. Dept. of the Interior, U.S. Geological Survey; c2005.
Avey MT, Quince AF, Sturdy CB. 2008. Seasonal and diurnal patterns of black-capped chickadee (poecile atricapillus) vocal production. Behav Processes 77(2):149-55.
Bailey D, Billeter R, Aviron S, Schweiger O, Herzog F. 2007. The influence of thematic resolution on metric selection for biodiversity monitoring in agricultural landscapes. Landscape Ecol 22(3):461-73.
147 Bergstrom JC. 2009. Preserving multifunctional agriculture: Discussion. Am J Agric Econ 91(5):1375-6.
Blumstein DT, Mennill DJ, Clemins P, Girod L, Yao K, Patricelli G, Deppe JL, Krakauer AH, Clark C, Cortopassi KA, et al. 2011. Acoustic monitoring in terrestrial environments using microphone arrays: Applications, technological considerations and prospectus. J Appl Ecol 48(3):758-67.
Bridges A and Dorcas M. 2000. Temporal variation in anuran calling behavior: Implications for surveys and monitoring programs. Copeia (2):587-92.
Brittain CA, Vighi M, Bommarco R, Settele J, Potts SG. 2010. Impacts of a pesticide on pollinator species richness at different spatial scales. Basic Appl Ecol 11(2):106-15.
Brumm H. 2006. Animal communication: City birds have changed their tune. Current Biology 16(23):R1003-4.
Brussaard L, Behan-Pelletier VM, Bignell DE, Brown VK, Didden W, Folgarait P, Fragoso C, Freckman DW, Gupta VVSR, Hattori T, et al. 1997. Biodiversity and ecosystem functioning in soil. Ambio 26(8):563-70.
Brussaard L, de Ruiter PC, Brown GG. 2007. Soil biodiversity for agricultural sustainability. Agric Ecosyst Environ 121(3):233-44.
Butchart SHM, Stattersfield AJ, Baillie J, Bennun LA, Stuart SN, Akcakaya HR, Hilton- Taylor C, Mace GM. 2005. Using red list indices to measure progress towards the 2010 target and beyond. Philos Trans R Soc B-Biol Sci 360(1454):255-68.
Cai J, Ee D, Lau A, Mason R, Pham B, Roe P, Zhang J, Gage S. 2007. Acoustic sensor networks for environmental monitoring. NEW YORK; 1515 BROADWAY, NEW YORK, NY 10036-9998 USA: ASSOC COMPUTING MACHINERY.
Cardinale BJ, Harvey CT, Gross K, Ives AR. 2003. Biodiversity and biocontrol: Emergent impacts of a multi-enemy assemblage on pest suppression and crop yield in an agroecosystem. Ecol Lett 6(9):857-65.
Carvalheiro, L. G., Veldtman, R., Shenkute, A. G., Tesfay, G. B., Pirk, C. W. W., Donaldson, J. S., et al. (2011). Natural and within-farmland biodiversity enhances crop productivity. Ecology Letters, 14(3), 251-259.
Celis-Murillo A, Deppe JL, Allen MF. 2009. Using soundscape recordings to estimate bird species abundance, richness, and composition. J Field Ornithol 80(1):64-78.
148 Chesmore ED and Nellenbach C. 2001. Acoustic methods for the automated detection and identification of insects. Acta Hort (562):223-31.
Crowder DW, Northfield TD, Strand MR, Snyder WE. 2010. Organic agriculture promotes evenness and natural pest control. Nature 466(7302):109-U123.
Cunnington GM and Fahrig L. 2010. Plasticity in the vocalizations of anurans in response to traffic noise. Acta Oecologica-International Journal of Ecology 36(5):463-70. de Groot RS, Wilson MA, Boumans RMJ. 2002. A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecol Econ 41(3):393-408.
Dobson A. 2005. Monitoring global rates of biodiversity change: Challenges that arise in meeting the convention on biological diversity (CBD) 2010 goals. Philos Trans R Soc B-Biol Sci 360(1454):229-41.
Dudley N, Baldock D, Nasi R, Stolton S. 2005. Measuring biodiversity and sustainable management in forests and agricultural landscapes. Philos Trans R Soc B-Biol Sci 360(1454):457-70.
Duelli P. 1997. Biodiversity evaluation in agricultural landscapes: An approach at two different scales. Agric Ecosyst Environ 62(2-3):81-91.
Duelli P and Obrist MK. 2003. Biodiversity indicators: The choice of values and measures. Agric Ecosyst Environ 98(1-3):87-98.
Favreau JM, Drew CA, Hess GR, Rubino MJ, Koch FH, Eschelbach KA. 2006. Recommendations for assessing the effectiveness of surrogate species approaches. Biodivers Conserv 15(12):3949-69.
Ferrington G. 2000. Acoustic ecology and environmental studies. Soundscape: The Journal of Acoustic Ecology 1(2):39-40.
Flynn DFB, Gogol-Prokurat M, Nogeire T, Molinari N, Richers BT, Lin BB, Simpson N, Mayfield MM, DeClerck F. 2009. Loss of functional diversity under land use intensification across multiple taxa. Ecol Lett 12(1):22-33.
Forrest TG. 1994. From sender to receiver - propagation and environmental-effects on acoustic-signals. Am Zool 34(6):644-54.
Fournier E and Loreau M. 1999. Effects of newly planted hedges on ground-beetle diversity (coleoptera, carabidae) in an agricultural landscape. Ecography 22(1):87- 97.
149 Gage SH. 2008. Acoustic Monitoring of the Kellogg Biological Station's Long-Term Ecological Research Site. Remote Environmental Assessment Laboratory Report 2008-1. East Lansing, MI.
Gaston KJ and Blackburn TM. 1995. Mapping biodiversity using surrogates for species richness: Macro-scales and new world birds. Proc R Soc Lond Ser B-Biol Sci 262(1365):335-41.
Gillespie TW, Foody GM, Rocchini D, Giorgi AP, Saatchi S. 2008. Measuring and modelling biodiversity from space. Progress in Physical Geography 32(2):203-21.
Goulding K, Jarvis S, Whitmore A. 2008. Optimizing nutrient management for farm systems. Philos Trans R Soc B-Biol Sci 363(1491):667-80.
Grixti JC, Wong LT, Cameron SA, Favret C. 2009. Decline of bumble bees (bombus) in the north american midwest. Biol Conserv 142(1):75-84.
Gruenewald B. 2010. Is pollination at risk? current threats to and conservation of bees. Gaia 19(1):61-7.
Gurr GM, Wratten SD, Luna JM. 2003. Multi-function agricultural biodiversity: Pest management and other benefits. Basic Appl Ecol 4(2):107-16.
Hagvar S. 1998. The relevance of the rio-convention on biodiversity to conserving the biodiversity of soils. Appl Soil Ecol 9(1-3):1-7.
Hagvar S. 1994. Preserving the natural heritage - the process of developing attitudes. Ambio 23(8):515-8.
Heino J and Mykra H. 2006. Assessing physical surrogates for biodiversity: Do tributary and stream type classifications reflect macroinvertebrate assemblage diversity in running waters? Biol Conserv 129(3):418-26.
Herzog SK, Kessler M, Cahill TM. 2002. Estimating species richness of tropical bird communities from rapid assessment data. Auk 119(3):749-69.
Hoffmann M, Hilton-Taylor C, Angulo A, Boehm M, Brooks TM, Butchart SHM, Carpenter KE, Chanson J, Collen B, Cox NA, et al. 2010. The impact of conservation on the status of the world's vertebrates. Science 330(6010):1503-9.
Holland JM, Thomas CFG, Birkett T, Southway S, Oaten H. 2005. Farm-scale spatiotemporal dynamics of predatory beetles in arable crops. J Appl Ecol 42(6):1140-52.
150 Hsu MY, Kam YC, Fellers GM. 2006. Temporal organization of an anuran acoustic community in a taiwanese subtropical forest. J Zool 269(3):331-9.
Hutto RL and Stutzman RJ. 2009. Humans versus autonomous recording units: A comparison of point-count results. J Field Ornithol 80(4):387-98.
Isaacs R, Tuell J, Fiedler A, Gardiner M, Landis D. 2009. Maximizing arthropod- mediated ecosystem services in agricultural landscapes: The role of native plants. Front Ecol Environ 7(4):196-203.
Jeanneret P, Schupbach B, Luka H. 2003. Quantifying the impact of landscape and habitat features on biodiversity in cultivated landscapes. Agric Ecosyst Environ 98(1-3):311-20.
Johnston RJ and Duke JM. 2009. Informing preservation of multifunctional agriculture when primary research is unavailable: An application of meta-analysis. Am J Agric Econ 91(5):1353-9.
Kadoya T and Washitani I. 2011. The satoyama index: A biodiversity indicator for agricultural landscapes. Agric Ecosyst Environ 140(1-2):20-6.
Kasten EP, McKinley PK and Gage SH. 2007. Automated ensemble extraction and analysis of acoustic data streams. ; June 2007; Toronto, Ontario, Canada. .
Kennedy TA, Naeem S, Howe KM, Knops JMH, Tilman D, Reich P. 2002. Biodiversity as a barrier to ecological invasion. Nature 417(6889):636-8.
Kerr JT, Sugar A, Packer L. 2000. Indicator taxa, rapid biodiversity assessment, and nestedness in an endangered ecosystem. Conserv Biol 14(6):1726-34.
Kevan PG. 1999. Pollinators as bioindicators of the state of the environment: Species, activity and diversity. Agric Ecosyst Environ 74(1-3):373-93.
Kim J, Williams N, Kremen C. 2006. Effects of cultivation and proximity to natural habitat on ground-nesting native bees in california sunflower fields. J Kans Entomol Soc 79(4):309-20.
Klein A, Vaissiere BE, Cane JH, Steffan-Dewenter I, Cunningham SA, Kremen C, Tscharntke T. 2007. Importance of pollinators in changing landscapes for world crops. Proc R Soc B-Biol Sci 274(1608):303-13.
151 Kremen C and Ostfeld RS. 2005. A call to ecologists: Measuring, analyzing, and managing ecosystem services. Front Ecol Environ 3(10):540-8.
Lammers MO, Brainard RE, Au WWL, Mooney TA, Wong KB. 2008. An ecological acoustic recorder (EAR) for long-term monitoring of biological and anthropogenic sounds on coral reefs and other marine habitats. J Acoust Soc Am 123(3):1720-8.
Letourneau DK and Bothwell SG. 2008. Comparison of organic and conventional farms: Challenging ecologists to make biodiversity functional. Front Ecol Environ 6(8):430- 8.
Loh J, Green RE, Ricketts T, Lamoreux J, Jenkins M, Kapos V, Randers J. 2005. The living planet index: Using species population time series to track trends in biodiversity. Philosophical Transactions of the Royal Society B: Biological Sciences 360(1454):289-95.
MA. 2005. Millenium ecosystem assessment: Ecosystems and human well-being: Global assessment reports. .
MacSwiney G. MC, Clarke FM, Racey PA. 2008. What you see is not what you get: The role of ultrasonic detectors in increasing inventory completeness in neotropical bat assemblages. J Appl Ecol 45(5):1364-71.
Mader P, Fliessbach A, Dubois D, Gunst L, Fried P, Niggli U. 2002. Soil fertility and biodiversity in organic farming. Science 296(5573):1694-7.
Marten K and Marler P. 1977. Sound-transmission and its significance for animal vocalization .1. temperate habitats. Behav Ecol Sociobiol 2(3):271-90.
McGuire KL and Treseder KK. 2010. Microbial communities and their relevance for ecosystem models: Decomposition as a case study. Soil Biol Biochem 42(4):529-35.
McLaughlin A and Mineau P. 1995. The impact of agricultural practices on biodiversity. Agric Ecosyst Environ 55(3):201-12.
Miki T, Ushio M, Fukui S, Kondoh M. 2010. Functional diversity of microbial decomposers facilitates plant coexistence in a plant-microbe-soil feedback model. Proc Natl Acad Sci U S A 107(32):14251-6.
Miller, Michael. 2008. Modified Acoustic Entropy Index.
Moonen A and Barberi P. 2008. Functional biodiversity: An agroecosystem approach. Agric , Ecosyst Environ 127(1):7-21.
152 Nannipieri P, Ascher J, Ceccherini MT, Landi L, Pietramellara G, Renella G. 2003. Microbial diversity and soil functions. Eur J Soil Sci 54(4):655-70.
Naveh Z. 1994. From biodiversity to ecodiversity: A landscape-ecology approach to conservation and restoration. Restor Ecol 2(3):180-9.
Nicholls CI, Parrella M, Altieri MA. 2001. The effects of a vegetational corridor on the abundance and dispersal of insect biodiversity within a northern california organic vineyard. Landscape Ecol 16(2):133-46.
Nityananda V and Balakrishnan R. 2008. Leaders and followers in katydid choruses in the field: Call intensity, spacing and consistency. Anim Behav 76:723-35.
Noss RF. 1990. Indicators for monitoring biodiversity - a hierarchical approach. Conserv Biol 4(4):355-64.
O'Connell AF, Gilbert AT, Hatfield JS. 2004. Contribution of natural history collection data to biodiversity assessment in national parks. Conserv Biol 18(5):1254-61.
O'Neill G. 2008. Jump-starting: ENVIRONMENTAL MONITORING. Ecos (143):14.
Paine RT. 1966. Food web complexity and species diversity. Am Nat 100(910):65,&.
Pettorelli N, Vik JO, Mysterud A, Gaillard JM, Tucker CJ, Stenseth NC. 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol 20(9):503-10.
Pijanowski BC, Villanueva-Rivera LJ, Dumyahn SL, Farina A, Krause BL, Napoletano BM, Gage SH, Pieretti N. 2011. Soundscape ecology: The science of sound in the landscape. Bioscience 61(3):203-16.
Ponder W. 1999. Using museum collection data to assist in biodiversity assessment. Other 99%: The Conservation and Biodiversity of Invertebrates :253-6.
Porter J, Arzberger P, Braun HW, Bryant P, Gage S, Hansen T, Hanson P, Lin CC, Lin FP, Kratz T, et al. 2005. Wireless sensor networks for ecology. Bioscience 55(7):561-72.
Post AL, Wassenberg TJ, Passlow V. 2006. Physical surrogates for macrofaunal distributions and abundance in a tropical gulf. Mar Freshw Res 57(5):469-83.
REAL [Internet] [cited 2008 9/20]. Available from: http://www.real.msu.edu/overview .
153 Robinson D and Hall M. 2002. Sound signalling in orthoptera. Advances in Insect Physiology, Vol 29 29:151-278.
Rodriguez C and Wiegand K. 2009. Evaluating the trade-off between machinery efficiency and loss of biodiversity-friendly habitats in arable landscapes: The role of field size. Agric Ecosyst Environ 129(4):361-6.
Sanchez, R.C. Maher, and S. Gage. Ecological and environmental acoustic remote sensor (EcoEARS) application for long-term monitoring and assessment of wildlife. U.S. department of defense threatened, endangered and at-risk species research symposium and workshop ; June, 2005; Baltimore, MD.
Santi E, Maccherini S, Rocchini D, Bonini I, Brunialti G, Favilli L, Perini C, Pezzo F, Piazzini S, Rota E, et al. 2010. Simple to sample: Vascular plants as surrogate group in a nature reserve. J Nat Conserv 18(1):2-11.
Selmi S and Boulinier T. 2003. Does time of season influence bird species number determined from point-count data? A capture-recapture approach. J Field Ornithol 74(4):349-56. Shuler RE, Roulston TH, Farris GE. 2005. Farming practices influence wild pollinator populations on squash and pumpkin. J Econ Entomol 98(3):790-5.
Slagsvold T. 1996. Dawn and dusk singing of male american robins in relation to female behavior. Wilson Bull 108(3):507-15.
Spangenberg JH. 2007. Biodiversity pressure and the driving forces behind. Ecol Econ 61(1):146-58.
Stevens C and Paszkowski C. 2004. Using chorus-size ranks from call surveys to estimate reproductive activity of the wood frog (rana sylvatica). J Herpetol 38(3):404-10.
Stobbelaar DJ, Groot JCJ, Makowski D, Tichit M. 2009. Multifunctional agriculture - from farm diagnosis to farm design and institutional innovation. J Environ Manage 90:S109-11.
Stockwell DRB and Peterson AT. 2002. Controlling bias in biodiversity data. WASHINGTON; 1718 CONNECTICUT AVE NW, SUITE 300, WASHINGTON, DC 20009 USA: ISLAND PRESS.
Sueur J, Pavoine S, Hamerlynck O, Duvail S. 2008. Rapid acoustic survey for biodiversity appraisal. PLoS One 3(12):e4065-.
154 Sueur J and Sanborn AF. 2003. Ambient temperature and sound power of cicada calling songs (hemiptera : Cicadidae : Tibicina). Physiol Entomol 28(4):340-3.
Sueur J. 2002. Cicada acoustic communication: Potential sound partitioning in a multispecies community from mexico (hemiptera : Cicadomorpha : Cicadidae). Biol J Linn Soc 75(3):379-94.
Swift MJ, Izac AMN, van Noordwijk M. 2004. Biodiversity and ecosystem services in agricultural landscapes - are we asking the right questions? Agric Ecosyst Environ 104(1):113-34.
Tilman D, Fargione J, Wolff B, D'Antonio C, Dobson A, Howarth R, Schindler D, Schlesinger WH, Simberloff D, Swackhamer D. 2001. Forecasting agriculturally driven global environmental change. Science 292(5515):281-4.
Tilman D, Reich PB, Knops JMH. 2006. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441(7093):629-32.
Torsvik V and Ovreas L. 2002. Microbial diversity and function in soil: From genes to ecosystems. Curr Opin Microbiol 5(3):240-5.
Tzoulas K and James P. 2010. Making biodiversity measures accessible to non- specialists: An innovative method for rapid assessment of urban biodiversity. Urban Ecosyst 13(1):113-27.
Ulgiati S and Brown MT. 1998. Monitoring patterns of sustainability in natural and man- made ecosystems. Ecol Model 108(1-3):23-36.
United Nations. 1992. Convention on biological diversity. rio de janeiro, 5 june 1992. Treaties and International Agreements Registered Or Filed Or Reported with the Secretariat of the United Nations XXVII.8.
Vadrevu KP, Cardina J, Hitzhusen F, Bayoh I, Moore R, Parker J, Stinner B, Stinner D, Hoy C. 2008. Case study of an integrated framework for quantifying agroecosystem health. Ecosystems 11(2):283-306. van der Heijden MGA, Klironomos JN, Ursic M, Moutoglis P, Streitwolf-Engel R, Boller T, Wiemken A, Sanders IR. 1998. Mycorrhizal fungal diversity determines plant biodiversity, ecosystem variability and productivity. Nature 396(6706):69-72. van der Ploeg JD, Laurent C, Blondeau F, Bonnafous P. 2009. Farm diversity, classification schemes and multifunctionality. J Environ Manage 90:S124-31.
155 Wardle DA, Bardgett RD, Klironomos JN, Setala H, van der Putten WH, Wall DH. 2004. Ecological linkages between aboveground and belowground biota. Science 304(5677):1629-33.
Whittaker RH. 1972. Evolution and measurement of species diversity. Taxon (2):213-51.
Wilson DM and Bart J. 1985. Reliability of singing bird surveys - effects of song phenology during the breeding-season. Condor 87(1):69-73.
Winfree R and Kremen C. 2009. Are ecosystem services stabilized by differences among species? A test using crop pollination. Proc R Soc B-Biol Sci 276(1655):229-37.
Living Planet Report [Internet]: World Wildlife Foundation [cited 2011 5/5]. Available from: http://wwf.panda.org/about_our_earth/all_publications/living_planet_report/ .
Zak DR, Holmes WE, White DC, Peacock AD, Tilman D. 2003. Plant diversity, soil microbial communities, and ecosystem function: Are there any links? Ecology 84(8):2042-50.
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Appendix A: Matlab Script for H’ Index (Miller 2008)
%scaled_readonelist_1K reads a list containing one filename per line for %calculation and comparison of RASBA values; computes Entropy values only, %then scales the Entropy by the mean of absolute value of the amplitude. %Filters the sound data through a high pass filter with a 1000 Hz cutoff %frequency. Saves the results in a file with a suffix of _1K-only.txt %------Start------%Pick filelist for input [MyFileName,MyPathName] = uigetfile('*.csv','Select single file list'); if isequal(MyFileName,0) fprintf('The user chose to Cancel...returning to command line.\n'); return; end %Myfilelist=importdata(fullfile(MyPathName,MyFileName)); %size(Myfilelist) %Read in the Masterfile list (Myfilelist{1}) and the second filelist %(Myfilelist{2}) fid = fopen(fullfile(MyPathName,MyFileName)); Myfilelist = textscan(fid, '%q %q','delimiter', ','); fclose(fid); Masterfilelist=char(Myfilelist{1}); Mas_dname=deblank(Masterfilelist(1,:));
%Open a text file for writing to receive the output data. Create the %filename from the two directory names and place it in the Master %directory. textfilename=[Mas_dname '_1K-only.txt']; outfilename=fullfile(MyPathName,textfilename); outfile=fopen(outfilename,'wt'); 157 fprintf(outfile,'"Master File","Ht(Master)","Hf(Master)","H(Master)","Scaled H(Master)"\n');
%Create high pass wind filter w/cutoff at 1000 Hz for a sampling rate of %22050 Hz (Nyquist freq=11025 Hz) [b,a] = cheby1(9,0.5,1000/11025,'high'); h = waitbar(0,'Please Wait...Calculating RASBA indices.'); %Start looping through the Master Files list for k=2:length(Myfilelist{1}) %Calculate the RASBA Acoustic Entropy indices, once for the Master File, %once for the second file
%------Start of RASBA on Masterfile------%Read the wav file into matrix y [y,Fs,bits] = wavread(fullfile(MyPathName,Mas_dname,Masterfilelist(k,:))) ; %Calculate mean amplitude on channel 1 MeanAmp=mean(y(:,1)); %Remove the DC offset y1=y(:,1)-MeanAmp;
%Apply filter to the sound file y2 = filter(b,a,y1); Myscalefactor=mean(abs(y2)); %Hilbert transform the filtered sound,compute the complex modulus of the %analytic signal, and sum it MyXi=abs(hilbert(y2)); MySumXi=sum(MyXi); %Compute the probability mass function (eq 2) MyA1=MyXi/MySumXi; %Compute the temporal entropy (eq 4) MyHt1=-sum(MyA1 .* log2(MyA1))/log2(length(MyA1));
%Compute the spectrum using STFT, zero overlap, 512 sample window, N=256 s = spectrogram(y2,512,0,512,Fs); %(y2,window,noverlap,nfft,fs) size(S) is 257 rows 1291 columns %Calculate the mean spectrum mean(S,2) gives column vector with mean of
158 %each row s=mean(s,2); %Calculate the complex magnitude and its sum MyS=abs(s); MySumS=sum(MyS); %Compute the probability mass function for the mean spectrum (eq 2 applied to the spectrum) MySf1=MyS/MySumS; %Compute the spectral entropy (eq5) MyHf1=-sum(MySf1 .* log2(MySf1))/log2(length(MySf1)); %Compute the entropy index H MyH1=MyHt1*MyHf1; %Now scale it to the amplitude using mean of the absolute value of the %sound amplitude MyscaledH1=MyH1*Myscalefactor; %------End of RASBA on Masterfile------%update the wait bar and loop h = waitbar(k/length(Myfilelist{1}),h,'Please Wait...Calculating RASBA indices.'); fprintf(outfile,'%s,%1.4f,%1.4f,%1.4f,%1.4f\n',fullfile(MyP athName,Mas_dname,Masterfilelist(k,:)),MyHt1,MyHf1,MyH1,Mys caledH1); end;
%update the wait bar and loop h = waitbar(k/length(Myfilelist{1}),h,'Please Wait...Calculating RASBA indices.');
%When all of the files have been processed, close the output file. h = waitbar(100,h,'Done...Closing output file.'); status=fclose(outfile); pause(2); close(h);
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Appendix B: Plant Diversity Data
Site 23: Site 22: Plant Plant Quantity Plant Species Quantity Plant Species (n Yellow Woodsorrel 50 Green Foxtail 1 Ground Ivy 20 Dandelions 11 Smooth Pigweed 4 Rescue Grass 6 Oats (fallow) 4 Yellow Woodsorrel 6 Common Ragweed 4 Ground Ivy 20 Green Foxtail 22 Soy Beans 15 Rescue Grass 1 Spotted Spurge 1 Eaten small dense leaves 6 Poison Ivy 8 Lily 13 Blackseed Plantain 1 Poison Ivy 15 Locus Tree 1 American Black Cherry 3 Pine Tree 1 Virginaia Creeper 12 Rose Thorn Bush 1 Maple Tree 1 American Black Cherry 2 Yellow Flowers ? 1 Virginia Creeper 1 Short Tree ? 1 Jewel Weed 3 Rose, thorn bush, Alt. Leaves ? 1 berries 2 Sasafras 1 Prickly 1 Raspberry Thorn Bush 1 PA Smartweed 1 Total number of individuals 81 Flender Rush 5 Total number of individuals 166
160 Site 24 Site 25 Plant Plant Plant Species Quantity Plant Species Quantity Corn at five meters 2 Giant Ragweed 26 Dandelions 56 Thistle 4 Thistle 2 Common Ragweed 16 Pigweed 12 Knotroot Foxtail 59 Yellow Nutsedge 3 Clover 32 Different Pigweed 1 Ground Ivy 15 Grass, on east side Big Tall Grass 28 Total number of Jewel Weed 15 individuals 76 Spike Ball? 14 Slightly Jagged (see pic)? 2 dense leaf cover, long? 1 Poison Ivy 16 Giant Foxtail 2 Total number of individuals 230
161 Site 20 Site 21 Plant Plant Plant Species Quantity Plant Species Quantity Soybeans at 3 m 75 Corn at 3.5 m 6 Green Foxtail 2 Yellow Woodsorrel 6 Yellow Woodsorrel 1 PA Smartweed 2 Ground Ivy 6 Poison Ivy 5 Common Teazel? 1 Thorny 2 Leaf 1 American Germander 2 Jagged edge, 3 leaf ? 2 Common Pokeweed 6 Virginia Creeper 25 Poison Ivy 35 Cherry Tree 1 Maple Tree 2 Oak Tree 2 Pine? Tree 3 Ball head plants? 2 (Serated edges)? 4 little green plants? 3 Virginia Creeper 8 Total number of Raspberry Thorn bush 2 individuals 55 Cherry Tree 2 Broad Leaf Tree? 1 Moss (not counted) 25 Total number of individuals 175
In some cases, plants that were clearly different species, but could not be accurately identified, were described and counted as individuals of an unnamed species. These are indicated in tables with a description followed by a “?”.
162
Appendix C: Insect diversity data
First pitfall sampling Genera OH20_1 OH21_1 OH22_1 OH23_1 OH24_1 OH25_1 Lepidoptera 26 3 4 0 0 0 Pedes 4 2 2 1 0 1 apterygota 0 0 0 0 0 0 Oliopones 1 3 13 12 5 0 Isopoda 2 5 4 10 1 0 Araneae 5 6 6 17 7 9 Mites 2 1 0 1 0 1 plumonata 4 1 8 3 3 0 hemiptera 0 0 1 0 2 0 orthoptera 4 5 1 3 2 0 homoptera 4 0 2 2 1 1 coleoptera 59 12 46 44 10 19 Diptera 21 8 11 6 13 10 Isoptera 0 3 0 0 0 7 hymenoptera 15 6 91 23 13 23 mecoptera 0 0 1 0 0 0 Pscoptera 0 0 1 0 0 0 Embioptera 3 0 2 2 0 10 Dermaptera 0 0 0 0 0 0 Archegnathera 0 0 0 0 0 0 collembota 4 10 12 23 15 10 Olioptera 0 0 0 0 0 0 Annelid 1 0 0 5 0 2 grylloblattodae 0 1 0 0 0 0 Thysanum 0 0 1 0 0 0
163
Second pitfall sampling Genera OH20_2 OH21_2 OH22_2 OH23_2 OH24_2 OH25_2 Lepidoptera 25 14 7 0 1 0 Pedes 6 0 0 3 3 0 apterygota 1 0 0 0 0 0 Oliopones 8 3 0 0 0 0 Isopoda 7 0 0 0 0 10 Araneae 12 24 5 9 10 17 Mites 3 2 0 3 0 1 plumonata 3 0 0 4 7 2 hemiptera 2 0 1 1 1 0 orthoptera 1 6 0 12 10 1 homoptera 1 0 0 1 0 0 coleoptera 45 34 8 2 16 15 Diptera 46 1 12 18 14 9 Isoptera 0 2 1 0 2 0 hymenoptera 0 29 35 13 18 17 mecoptera 0 0 1 0 0 0 Pscoptera 0 0 0 1 3 2 Embioptera 0 0 0 1 2 22 Dermaptera 0 0 0 3 0 0 Archegnathera 0 0 0 1 0 0 collembota 0 0 0 9 79 0 olioptera 0 0 0 0 2 11 anelid 0 0 0 0 0 0 grylloblattodae 0 0 0 0 0 0 thysanum 0 0 0 0 0 0
164
Appendix D: Regression Data
Regression Data 2009 Std. Variable B Error t Sig. AHI0 -.007 .016 -.457 .672 AHI3 -.005 .017 -.271 .800 BD0 .013 .008 1.655 .173 BD3 .012 .010 1.251 .279 SOIL0 .010 .009 1.017 .366 SOIL3 .014 .019 .739 .501 ELV0 -.001 .004 -.219 .837 ELV3 .001 .005 .175 .870 SOC0 .004 .007 .593 .585 SOC3 .004 .007 .608 .576 MKT0 .003 .006 .487 .652 MKT3 .005 .006 .805 .466 CAUV0 -.002 .006 -.269 .801 CAUV3 -.003 .006 -.399 .710
Regression Data 2009 & 2010 Comb. Std. Variable B Error t Sig. AHI0 -.010 .012 -.855 .441 AHI3 -.007 .014 -.532 .623 BD0 .014 .005 3.125 .035 BD3 .014 .005 2.667 .056 SOIL0 .004 .008 .460 .669 SOIL3 .004 .016 .279 .794 ELV0 -.001 .003 -.290 .787 ELV3 .001 .004 .136 .899 SOC0 .004 .006 .732 .505 SOC3 .004 .006 .770 .484 MKT0 .001 .005 .152 .887 MKT3 .002 .005 .397 .711 CAUV0 .002 .005 .310 .772 CAUV3 .001 .005 .226 .832
165 Regression Data 2010 Std. Variable B Error Sig. AHI0 -.013 .011 .306 AHI3 -.009 .013 .511 BD0 .015 .003 .012 BD3 .017 .003 .003 SOIL0 -.002 .008 .841 SOIL3 -.005 .016 .782 ELV0 -.001 .003 .763 ELV3 .000 .004 .967 SOC0 .004 .006 .493 SOC3 .004 .005 .463 MKT0 -.002 .005 .777 MKT3 -.001 .005 .877 CAUV0 .005 .004 .334 CAUV3 .005 .005 .340
166 Plant Transect Regression Std. Variable B Error t Sig. AHI0 -5.391 1.623 -3.323 .029 AHI3 -5.558 2.057 -2.701 .054 BD0 2.169 1.745 1.243 .282 BD3 2.028 1.983 1.022 .364 SOIL0 -1.925 1.832 -1.051 .353 SOIL3 -3.131 3.665 -.854 .441 ELV0 .844 .609 1.385 .238 ELV3 1.256 .686 1.830 .141 SOC0 -1.570 1.241 -1.266 .274 SOC3 -1.460 1.267 -1.152 .314 MKT0 1.528 1.007 1.518 .204 MKT3 1.773 .954 1.858 .137 CAUV0 .588 1.180 .499 .644 CAUV3 .798 1.213 .658 .547
Insect Pitfall Regression Std. Variable B Error t Sig. AHI0 -1.426 .850 -1.678 .169 AHI3 -1.856 .791 -2.347 .079 BD0 -.387 .698 -.554 .609 BD3 -.001 .786 -.001 .999 SOIL0 -1.227 .396 -3.101 .036 SOIL3 -2.415 .719 -3.358 .028 ELV0 -.075 .259 -.290 .786 ELV3 -.155 .319 -.487 .652 SOC0 -.683 .389 -1.754 .154 SOC3 -.693 .382 -1.814 .144 MKT0 -.311 .418 -.744 .498 MKT3 -.271 .439 -.618 .570 CAUV0 -.031 .429 -.072 .946 CAUV3 -.064 .449 -.143 .893
167
Avian Call Number Regression Std. Variable B Error t Sig. AHI0 -85.57 224.3 -.38 .722 AHI3 7.78 250.9 .03 .977 BD0 223.58 98.7 2.26 .086 BD3 219.73 118.6 1.85 .138 SOIL0 -37.36 149.0 -.25 .814 SOIL3 -18.84 289.1 -.06 .951 ELV0 30.44 51.5 .59 .587 ELV3 52.79 62.1 .84 .444 SOC0 86.59 97.3 .88 .424 SOC3 95.25 94.8 1.00 .372 MKT0 20.08 91.2 .22 .837 MKT3 8.45 94.4 .09 .933 CAUV0 130.44 59.5 2.19 .093 CAUV3 160.92 45.9 3.50 .025
168
Spectral Entropy Hf 2009 Regression Std. Variable B Error t Sig. AHI0 .000 .020 .009 .993 AHI3 .004 .022 .171 .872 BD0 .012 .012 1.061 .349 BD3 .009 .014 .660 .545 SOIL0 .016 .011 1.492 .210 SOIL3 .024 .023 1.045 .355 ELV0 .000 .005 -.066 .950 ELV3 .001 .006 .243 .820 SOC0 .008 .009 .961 .391 SOC3 .008 .008 .979 .383 MKT0 .005 .008 .627 .565 MKT3 .007 .008 .949 .396 CAUV0 -.005 .007 -.647 .553 CAUV3 -.005 .008 -.640 .557
Temporal Entropy Ht 2009 Regression Std. Variable B Error Beta Sig. AHI0 -.001 .023 -.028 .959 AHI3 .003 .025 .055 .918 BD0 .014 .013 .492 .321 BD3 .011 .015 .354 .492 SOIL0 .017 .012 .574 .234 SOIL3 .025 .026 .433 .392 ELV0 -.001 .005 -.070 .895 ELV3 .001 .007 .088 .868 SOC0 .009 .009 .437 .386 SOC3 .009 .009 .443 .379 MKT0 .005 .009 .258 .621 MKT3 .007 .009 .393 .441 CAUV0 -.005 .008 -.295 .571 CAUV3 -.005 .009 -.298 .566
169 Spectral Entropy Hf 2010 Regression Std. Variable B Error t Sig. AHI0 -.002 .012 -.137 .897 AHI3 .000 .014 .024 .982 BD0 .007 .007 1.001 .373 BD3 .010 .007 1.352 .248 SOIL0 .004 .008 .528 .625 SOIL3 .001 .016 .067 .950 ELV0 -.003 .002 -1.502 .208 ELV3 -.004 .003 -1.147 .315 SOC0 .008 .004 1.876 .134 SOC3 .008 .004 1.827 .142 MKT0 -.005 .004 -1.171 .307 MKT3 -.004 .005 -.803 .467 CAUV0 -.002 .005 -.324 .762 CAUV3 -.002 .005 -.379 .724
Temporal Entropy Ht 2010 Regression Std. Variable B Error t Sig. AHI0 -.001 .011 -.075 .944 AHI3 .001 .012 .097 .927 BD0 .006 .006 1.048 .354 BD3 .008 .006 1.344 .250 SOIL0 .004 .007 .638 .558 SOIL3 .002 .013 .174 .870 ELV0 -.003 .002 -1.437 .224 ELV3 -.003 .003 -1.075 .343 SOC0 .007 .003 1.994 .117 SOC3 .007 .004 1.945 .124 MKT0 -.004 .004 -1.068 .346 MKT3 -.003 .004 -.727 .507 CAUV0 -.001 .004 -.316 .768 CAUV3 -.002 .004 -.373 .728
170 2009 AHI0 Seasonal Regressions
Variable B Std. Error t Sig. 1 week (1) - 8/6 - 8/12 -.035 .028 -1.254 .278 1 week (2) - 8/13 - 8/19 -.021 .033 -.633 .561 1 week (3) - 8/20 - 8/26 -.024 .037 -.651 .550 1 week (4) - 8/27 - 9/2 -.023 .021 -1.081 .341 1 week (5) - 9/3 -9/9 -.021 .020 -1.069 .345 1 week (6) - 9/10 - 9/16 -.006 .009 -.675 .537 1 week (7) - 9/17 - 9/23 -.001 .013 -.114 .915 1 week (8) - 9/24 - 9/30 .006 .030 .200 .851 1 week (9) - 10/1 - 10/7 .010 .017 .612 .574 1 week (10) - 10/8 - 10/14 .002 .004 .473 .661 1 week (11) - 10/15 - 10/21 .004 .002 2.721 .053 1 week (12) - 10/22 - 10/28 .010 .009 1.177 .305 1 week (13) - 10/29 - 11/4 .003 .012 .257 .810 2 weeks (1) - 8/6 - 8/19 -.028 .028 -.990 .378 2 weeks (2) - 8/20 - 9/2 -.023 .029 -.812 .462 2 weeks (3) - 9/3 - 9/16 -.014 .014 -.956 .393 2 weeks (4) - 9/17 - 9/30 .002 .020 .112 .916 2 weeks (5) - 10/1 - 10/14 .006 .010 .602 .580 2 weeks (6) - 10/15 - 10/28 .007 .005 1.428 .226 3 weeks (1) - 8/6 - 8/26 -.026 .029 -.903 .417 3 weeks (2) - 8/27 - 9/16 -.017 .016 -1.017 .367 3 weeks (3) - 9/17 - 10/7 .005 .019 .261 .807 3 weeks (3) - 9/17 - 10/7 .006 .005 1.163 .310 3 weeks (4) - 10/8 - 10/ 28 -.025 .027 -.948 .397 4 weeks (2) - 9/3 - 9/30 -.006 .017 -.340 .751 4 weeks (3) - 10/1 - 10/ 28 .007 .007 .897 .420 4 weeks (3) - 10/1 - 10/ 28 -.025 .025 -.971 .387 5 weeks (1) - 8/6 - 9/9 .002 .014 .155 .884 6 weeks (1) - 8/6 - 9/16 -.022 .022 -.957 .393 6 weeks (2) - 9/17 - 10/28 .005 .012 .447 .678 7 weeks – 8/6 - 9/23 -.019 .021 -.897 .421 8 weeks – 8/6 - 9/30 -.016 .021 -.743 .499 9 weeks – 8/6 - 10/7 -.013 .020 -.631 .562 9 weeks – 8/6 - 10/7 -.011 .018 -.610 .575 11 weeks - 8/6 - 10/21 -.010 .017 -.583 .591 12 weeks - 8/6 - 10/28 -.008 .016 -.508 .638 13 weeks - 8/6 - 11/4 -.007 .016 -.464 .667
171
2009 AHI3 Seasonal Regressions
Variable B Std. Error T Sig. 1 week (1) - 8/6 - 8/12 -.035 .031 -1.127 .323 1 week (2) - 8/13 - 8/19 -.016 .037 -.425 .693 1 week (3) - 8/20 - 8/26 -.018 .042 -.435 .686 1 week (4) - 8/27 - 9/2 -.019 .024 -.789 .474 1 week (5) - 9/3 -9/9 -.020 .023 -.865 .436 1 week (6) - 9/10 - 9/16 -.005 .010 -.460 .669 1 week (7) - 9/17 - 9/23 .000 .014 -.004 .997 1 week (8) - 9/24 - 9/30 .012 .033 .359 .738 1 week (9) - 10/1 - 10/7 .013 .018 .738 .502 1 week (10) - 10/8 - 10/14 .002 .004 .488 .651 1 week (11) - 10/15 - 10/21 .005 .002 2.762 .051 1 week (12) - 10/22 - 10/28 .012 .010 1.237 .284 1 week (13) - 10/29 - 11/4 .005 .013 .353 .742 2 weeks (1) - 8/6 - 8/19 -.025 .032 -.799 .469 2 weeks (2) - 8/20 - 9/2 -.019 .033 -.569 .600 2 weeks (3) - 9/3 - 9/16 -.012 .016 -.746 .497 2 weeks (4) - 9/17 - 9/30 .006 .022 .264 .805 2 weeks (5) - 10/1 - 10/14 .008 .011 .709 .518 2 weeks (6) - 10/15 - 10/28 .008 .006 1.490 .211 3 weeks (1) - 8/6 - 8/26 -.023 .033 -.689 .529 3 weeks (2) - 8/27 - 9/16 -.014 .019 -.770 .484 3 weeks (3) - 9/17 - 10/7 .008 .020 .408 .704 3 weeks (3) - 9/17 - 10/7 .006 .005 1.207 .294 3 weeks (4) - 10/8 - 10/ 28 -.022 .031 -.716 .514 4 weeks (2) - 9/3 - 9/30 -.003 .019 -.169 .874 4 weeks (3) - 10/1 - 10/ 28 .008 .008 .993 .377 4 weeks (3) - 10/1 - 10/ 28 -.022 .029 -.741 .500 5 weeks (1) - 8/6 - 9/9 .004 .015 .302 .777 6 weeks (1) - 8/6 - 9/16 -.019 .026 -.727 .507 6 weeks (2) - 9/17 - 10/28 .007 .013 .577 .595 7 weeks - 8/6 - 9/23 -.016 .024 -.676 .536 8 weeks - 8/6 - 9/30 -.013 .024 -.529 .625 9 weeks - 8/6 - 10/7 -.010 .023 -.427 .691 9 weeks - 8/6 - 10/7 -.009 .021 -.411 .702 11 weeks - 8/6 - 10/21 -.007 .019 -.386 .719 12 weeks - 8/6 - 10/28 -.006 .018 -.318 .766 13 weeks - 8/6 - 11/4 -.005 .017 -.280 .793
172 2009 BD0 Seasonal Regressions
Variable B Std. Error t Sig 1 week (1) - 8/6 - 8/12 .020 .019 1.037 .358 1 week (2) - 8/13 - 8/19 .026 .018 1.451 .220 1 week (3) - 8/20 - 8/26 .032 .020 1.599 .185 1 week (4) - 8/27 - 9/2 .024 .010 2.414 .073 1 week (5) - 9/3 -9/9 .020 .011 1.779 .150 1 week (6) - 9/10 - 9/16 .009 .004 2.318 .081 1 week (7) - 9/17 - 9/23 .010 .007 1.390 .237 1 week (8) - 9/24 - 9/30 .019 .017 1.095 .335 1 week (9) - 10/1 - 10/7 .005 .011 .498 .645 1 week (10) - 10/8 - 10/14 .001 .003 .514 .634 1 week (11) - 10/15 - 10/21 -.001 .002 -.385 .720 1 week (12) - 10/22 - 10/28 .002 .007 .279 .794 1 week (13) - 10/29 - 11/4 .007 .007 .887 .425 2 weeks (1) - 8/6 - 8/19 .023 .017 1.362 .245 2 weeks (2) - 8/20 - 9/2 .028 .015 1.887 .132 2 weeks (3) - 9/3 - 9/16 .014 .007 1.961 .121 2 weeks (4) - 9/17 - 9/30 .014 .011 1.278 .270 2 weeks (5) - 10/1 - 10/14 .003 .007 .516 .633 2 weeks (6) - 10/15 - 10/28 .001 .004 .146 .891 3 weeks (1) - 8/6 - 8/26 .026 .017 1.562 .193 3 weeks (2) - 8/27 - 9/16 .018 .008 2.170 .096 3 weeks (3) - 9/17 - 10/7 .011 .011 1.041 .357 3 weeks (3) - 9/17 - 10/7 .001 .004 .238 .823 3 weeks (4) - 10/8 - 10/ 28 .025 .015 1.722 .160 4 weeks (2) - 9/3 - 9/30 .014 .008 1.727 .159 4 weeks (3) - 10/1 - 10/ 28 .002 .005 .383 .721 4 weeks (3) - 10/1 - 10/ 28 .024 .014 1.740 .157 5 weeks (1) - 8/6 - 9/9 .009 .008 1.171 .306 6 weeks (1) - 8/6 - 9/16 .022 .012 1.788 .148 6 weeks (2) - 9/17 - 10/28 .006 .007 .853 .442 7 weeks - 8/6 - 9/23 .020 .011 1.811 .144 8 weeks - 8/6 - 9/30 .020 .011 1.861 .136 9 weeks - 8/6 - 10/7 .018 .010 1.771 .151 9 weeks - 8/6 - 10/7 .017 .009 1.755 .154 11 weeks - 8/6 - 10/21 .015 .009 1.735 .158 12 weeks - 8/6 - 10/28 .014 .008 1.691 .166 13 weeks - 8/6 - 11/4 .013 .008 1.651 .174
173 2009 BD3 Seasonal Regressions
Variable B Std. Error t Sig 1 week (1) - 8/6 - 8/12 .030 .017 1.724 .160 1 week (2) - 8/13 - 8/19 .026 .020 1.283 .269 1 week (3) - 8/20 - 8/26 .027 .024 1.124 .324 1 week (4) - 8/27 - 9/2 .023 .012 1.911 .129 1 week (5) - 9/3 -9/9 .021 .012 1.735 .158 1 week (6) - 9/10 - 9/16 .010 .005 1.994 .117 1 week (7) - 9/17 - 9/23 .010 .008 1.233 .285 1 week (8) - 9/24 - 9/30 .009 .021 .440 .683 1 week (9) - 10/1 - 10/7 -.001 .012 -.080 .940 1 week (10) - 10/8 - 10/14 .000 .003 .127 .905 1 week (11) - 10/15 - 10/21 -.002 .002 -.975 .385 1 week (12) - 10/22 - 10/28 -.001 .007 -.179 .867 1 week (13) - 10/29 - 11/4 .004 .009 .441 .682 2 weeks (1) - 8/6 - 8/19 .028 .017 1.655 .173 2 weeks (2) - 8/20 - 9/2 .025 .018 1.393 .236 2 weeks (3) - 9/3 - 9/16 .015 .008 1.845 .139 2 weeks (4) - 9/17 - 9/30 .009 .014 .691 .527 2 weeks (5) - 10/1 - 10/14 .000 .007 -.041 .970 2 weeks (6) - 10/15 - 10/28 -.001 .004 -.333 .756 3 weeks (1) - 8/6 - 8/26 .028 .018 1.538 .199 3 weeks (2) - 8/27 - 9/16 .018 .009 1.899 .130 3 weeks (3) - 9/17 - 10/7 .006 .013 .458 .671 3 weeks (3) - 9/17 - 10/7 -.001 .004 -.221 .836 3 weeks (4) - 10/8 - 10/ 28 .027 .016 1.631 .178 4 weeks (2) - 9/3 - 9/30 .012 .010 1.202 .296 4 weeks (3) - 10/1 - 10/ 28 -.001 .006 -.154 .885 4 weeks (3) - 10/1 - 10/ 28 .025 .015 1.655 .173 5 weeks (1) - 8/6 - 9/9 .006 .009 .602 .579 6 weeks (1) - 8/6 - 9/16 .023 .013 1.691 .166 6 weeks (2) - 9/17 - 10/28 .003 .008 .306 .775 7 weeks - 8/6 - 9/23 .021 .012 1.701 .164 8 weeks - 8/6 - 9/30 .019 .012 1.560 .194 9 weeks - 8/6 - 10/7 .017 .012 1.406 .232 9 weeks - 8/6 - 10/7 .015 .011 1.383 .239 11 weeks - 8/6 - 10/21 .014 .010 1.356 .247 12 weeks - 8/6 - 10/28 .013 .010 1.290 .266 13 weeks - 8/6 - 11/4 .012 .010 1.240 .283
174 2009 SOIL0 Seasonal Regressions
Variable B Std. Error t Sig. 1 week (1) - 8/6 - 8/12 -.008 .021 -.369 .731 1 week (2) - 8/13 - 8/19 .012 .022 .547 .613 1 week (3) - 8/20 - 8/26 .017 .024 .692 .527 1 week (4) - 8/27 - 9/2 .006 .015 .394 .714 1 week (5) - 9/3 -9/9 .005 .015 .366 .733 1 week (6) - 9/10 - 9/16 .005 .006 .792 .473 1 week (7) - 9/17 - 9/23 .010 .007 1.435 .225 1 week (8) - 9/24 - 9/30 .028 .015 1.903 .130 1 week (9) - 10/1 - 10/7 .018 .007 2.449 .071 1 week (10) - 10/8 - 10/14 .004 .002 2.473 .069 1 week (11) - 10/15 - 10/21 .003 .001 5.083 .007 1 week (12) - 10/22 - 10/28 .012 .003 4.478 .011 1 week (13) - 10/29 - 11/4 .012 .005 2.289 .084 2 weeks (1) - 8/6 - 8/19 .002 .020 .099 .926 2 weeks (2) - 8/20 - 9/2 .011 .020 .581 .593 2 weeks (3) - 9/3 - 9/16 .005 .010 .492 .649 2 weeks (4) - 9/17 - 9/30 .019 .010 1.948 .123 2 weeks (5) - 10/1 - 10/14 .011 .004 2.636 .058 2 weeks (6) - 10/15 - 10/28 .008 .001 5.390 .006 3 weeks (1) - 8/6 - 8/26 .007 .021 .332 .757 3 weeks (2) - 8/27 - 9/16 .005 .012 .453 .674 3 weeks (3) - 9/17 - 10/7 .018 .008 2.195 .093 3 weeks (3) - 9/17 - 10/7 .007 .001 4.474 .011 3 weeks (4) - 10/8 - 10/ 28 .007 .019 .348 .746 4 weeks (2) - 9/3 - 9/30 .012 .009 1.264 .275 4 weeks (3) - 10/1 - 10/ 28 .009 .003 3.569 .023 4 weeks (3) - 10/1 - 10/ 28 .006 .018 .352 .743 5 weeks (1) - 8/6 - 9/9 .013 .006 2.041 .111 6 weeks (1) - 8/6 - 9/16 .006 .016 .380 .723 6 weeks (2) - 9/17 - 10/28 .013 .005 2.617 .059 7 weeks - 8/6 - 9/23 .007 .015 .457 .672 8 weeks - 8/6 - 9/30 .009 .014 .667 .541 9 weeks - 8/6 - 10/7 .010 .013 .793 .472 9 weeks - 8/6 - 10/7 .010 .012 .823 .457 11 weeks - 8/6 - 10/21 .009 .011 .853 .442 12 weeks - 8/6 - 10/28 .009 .010 .952 .395 13 weeks - 8/6 - 11/4 .010 .009 1.015 .368
175 2009 SOIL3 Seasonal Regressions
Variable B Std. Error t Sig. 1 week (1) - 8/6 - 8/12 -.031 .038 -.822 .457 1 week (2) - 8/13 - 8/19 .008 .043 .186 .861 1 week (3) - 8/20 - 8/26 .022 .048 .460 .669 1 week (4) - 8/27 - 9/2 .007 .030 .239 .823 1 week (5) - 9/3 -9/9 .003 .028 .103 .923 1 week (6) - 9/10 - 9/16 .007 .012 .628 .564 1 week (7) - 9/17 - 9/23 .017 .014 1.214 .292 1 week (8) - 9/24 - 9/30 .057 .026 2.149 .098 1 week (9) - 10/1 - 10/7 .033 .015 2.259 .087 1 week (10) - 10/8 - 10/14 .008 .003 2.567 .062 1 week (11) - 10/15 - 10/21 .006 .001 4.762 .009 1 week (12) - 10/22 - 10/28 .025 .004 5.590 .005 1 week (13) - 10/29 - 11/4 .022 .011 2.028 .112 2 weeks (1) - 8/6 - 8/19 -.012 .039 -.300 .779 2 weeks (2) - 8/20 - 9/2 .015 .038 .378 .724 2 weeks (3) - 9/3 - 9/16 .005 .020 .256 .810 2 weeks (4) - 9/17 - 9/30 .037 .018 2.012 .115 2 weeks (5) - 10/1 - 10/14 .021 .008 2.473 .069 2 weeks (6) - 10/15 - 10/28 .015 .002 6.761 .002 3 weeks (1) - 8/6 - 8/26 .000 .041 -.011 .991 3 weeks (2) - 8/27 - 9/16 .006 .023 .251 .814 3 weeks (3) - 9/17 - 10/7 .036 .016 2.197 .093 3 weeks (3) - 9/17 - 10/7 .013 .003 5.148 .007 3 weeks (4) - 10/8 - 10/ 28 .001 .038 .038 .971 4 weeks (2) - 9/3 - 9/30 .021 .019 1.121 .325 4 weeks (3) - 10/1 - 10/ 28 .018 .005 3.503 .025 4 weeks (3) - 10/1 - 10/ 28 .002 .036 .049 .964 5 weeks (1) - 8/6 - 9/9 .024 .012 1.992 .117 6 weeks (1) - 8/6 - 9/16 .003 .032 .084 .937 6 weeks (2) - 9/17 - 10/28 .024 .009 2.659 .056 7 weeks - 8/6 - 9/23 .005 .029 .164 .877 8 weeks - 8/6 - 9/30 .011 .028 .404 .707 9 weeks - 8/6 - 10/7 .014 .026 .527 .626 9 weeks - 8/6 - 10/7 .013 .023 .557 .607 11 weeks - 8/6 - 10/21 .012 .021 .584 .590 12 weeks - 8/6 - 10/28 .013 .020 .681 .533 13 weeks - 8/6 - 11/4 .014 .019 .743 .499
176 2009 ELV0 Seasonal Regressions
Variables B Std. Error t Sig. 1 week (1) - 8/6 - 8/12 .007 .015 .503 .641 1 week (2) - 8/13 - 8/19 .013 .015 .890 .424 1 week (3) - 8/20 - 8/26 .009 .018 .503 .642 1 week (4) - 8/27 - 9/2 .003 .011 .309 .773 1 week (5) - 9/3 -9/9 .003 .010 .333 .756 1 week (6) - 9/10 - 9/16 .002 .004 .418 .697 1 week (7) - 9/17 - 9/23 .003 .006 .452 .674 1 week (8) - 9/24 - 9/30 .003 .014 .210 .844 1 week (9) - 10/1 - 10/7 .004 .008 .551 .611 1 week (10) - 10/8 - 10/14 .000 .002 .062 .954 1 week (11) - 10/15 - 10/21 .001 .001 .909 .415 1 week (12) - 10/22 - 10/28 .002 .005 .419 .697 1 week (13) - 10/29 - 11/4 .002 .006 .403 .708 2 weeks (1) - 8/6 - 8/19 .010 .014 .749 .496 2 weeks (2) - 8/20 - 9/2 .006 .014 .432 .688 2 weeks (3) - 9/3 - 9/16 .003 .007 .362 .735 2 weeks (4) - 9/17 - 9/30 .003 .009 .299 .780 2 weeks (5) - 10/1 - 10/14 .002 .005 .465 .666 2 weeks (6) - 10/15 - 10/28 .002 .003 .524 .628 3 weeks (1) - 8/6 - 8/26 .010 .014 .687 .530 3 weeks (2) - 8/27 - 9/16 .003 .008 .342 .750 3 weeks (3) - 9/17 - 10/7 .003 .009 .382 .722 3 weeks (3) - 9/17 - 10/7 .001 .003 .412 .702 3 weeks (4) - 10/8 - 10/ 28 .008 .013 .615 .572 4 weeks (2) - 9/3 - 9/30 .003 .008 .351 .743 4 weeks (3) - 10/1 - 10/ 28 .002 .004 .501 .643 4 weeks (3) - 10/1 - 10/ 28 .007 .013 .570 .599 5 weeks (1) - 8/6 - 9/9 .002 .006 .380 .723 6 weeks (1) - 8/6 - 9/16 .006 .011 .563 .603 6 weeks (2) - 9/17 - 10/28 .002 .005 .397 .711 7 weeks - 8/6 - 9/23 .006 .010 .565 .602 8 weeks - 8/6 - 9/30 .005 .010 .540 .618 9 weeks - 8/6 - 10/7 .005 .009 .560 .605 9 weeks - 8/6 - 10/7 .005 .009 .553 .609 11 weeks - 8/6 - 10/21 .004 .008 .565 .603 12 weeks - 8/6 - 10/28 .004 .007 .573 .597 13 weeks - 8/6 - 11/4 .004 .007 .568 .601
177 2009 ELV3 Seasonal Regressions
Variables B Std. Error t Sig. 1 week (1) - 8/6 - 8/12 .007 .015 .489 .651 1 week (2) - 8/13 - 8/19 .013 .014 .919 .410 1 week (3) - 8/20 - 8/26 .009 .017 .542 .617 1 week (4) - 8/27 - 9/2 .004 .011 .345 .748 1 week (5) - 9/3 -9/9 .004 .010 .344 .748 1 week (6) - 9/10 - 9/16 .002 .004 .425 .693 1 week (7) - 9/17 - 9/23 .003 .006 .424 .694 1 week (8) - 9/24 - 9/30 .003 .014 .233 .827 1 week (9) - 10/1 - 10/7 .004 .008 .570 .599 1 week (10) - 10/8 - 10/14 .000 .002 .036 .973 1 week (11) - 10/15 - 10/21 .001 .001 .883 .427 1 week (12) - 10/22 - 10/28 .002 .005 .394 .714 1 week (13) - 10/29 - 11/4 .002 .006 .394 .714 2 weeks (1) - 8/6 - 8/19 .010 .014 .755 .493 2 weeks (2) - 8/20 - 9/2 .007 .014 .470 .663 2 weeks (3) - 9/3 - 9/16 .003 .007 .372 .728 2 weeks (4) - 9/17 - 9/30 .003 .009 .308 .774 2 weeks (5) - 10/1 - 10/14 .002 .005 .474 .660 2 weeks (6) - 10/15 - 10/28 .001 .003 .498 .645 3 weeks (1) - 8/6 - 8/26 .010 .014 .708 .518 3 weeks (2) - 8/27 - 9/16 .003 .008 .363 .735 3 weeks (3) - 9/17 - 10/7 .003 .009 .394 .714 3 weeks (3) - 9/17 - 10/7 .001 .003 .386 .719 3 weeks (4) - 10/8 - 10/ 28 .008 .013 .639 .558 4 weeks (2) - 9/3 - 9/30 .003 .008 .361 .736 4 weeks (3) - 10/1 - 10/ 28 .002 .004 .497 .645 4 weeks (3) - 10/1 - 10/ 28 .007 .013 .592 .586 5 weeks (1) - 8/6 - 9/9 .002 .006 .389 .717 6 weeks (1) - 8/6 - 9/16 .006 .011 .584 .590 6 weeks (2) - 9/17 - 10/28 .002 .005 .400 .709 7 weeks - 8/6 - 9/23 .006 .010 .583 .591 8 weeks - 8/6 - 9/30 .006 .010 .560 .606 9 weeks - 8/6 - 10/7 .005 .009 .580 .593 9 weeks - 8/6 - 10/7 .005 .009 .573 .597 11 weeks - 8/6 - 10/21 .005 .008 .584 .591 12 weeks - 8/6 - 10/28 .004 .007 .590 .587 13 weeks - 8/6 - 11/4 .004 .007 .583 .591
178 2009 CAUV0 Seasonal Regressions
Variable B Std. Error t Sig. 1 week (1) - 8/6 - 8/12 -.003 .013 -.215 .840 1 week (2) - 8/13 - 8/19 -.006 .013 -.446 .679 1 week (3) - 8/20 - 8/26 -.004 .015 -.303 .777 1 week (4) - 8/27 - 9/2 .001 .009 .091 .932 1 week (5) - 9/3 -9/9 -.002 .009 -.188 .860 1 week (6) - 9/10 - 9/16 .001 .004 .162 .879 1 week (7) - 9/17 - 9/23 .000 .005 -.022 .984 1 week (8) - 9/24 - 9/30 .000 .012 .002 .998 1 week (9) - 10/1 - 10/7 -.004 .006 -.597 .583 1 week (10) - 10/8 - 10/14 -.001 .002 -.333 .756 1 week (11) - 10/15 - 10/21 -.001 .001 -.746 .497 1 week (12) - 10/22 - 10/28 -.001 .004 -.303 .777 1 week (13) - 10/29 - 11/4 -.002 .005 -.397 .712 2 weeks (1) - 8/6 - 8/19 -.004 .012 -.356 .740 2 weeks (2) - 8/20 - 9/2 -.002 .012 -.153 .886 2 weeks (3) - 9/3 - 9/16 -.001 .006 -.084 .937 2 weeks (4) - 9/17 - 9/30 .000 .008 -.005 .996 2 weeks (5) - 10/1 - 10/14 -.002 .004 -.560 .605 2 weeks (6) - 10/15 - 10/28 -.001 .002 -.398 .711 3 weeks (1) - 8/6 - 8/26 -.004 .012 -.353 .742 3 weeks (2) - 8/27 - 9/16 .000 .007 -.009 .993 3 weeks (3) - 9/17 - 10/7 -.001 .007 -.179 .866 3 weeks (3) - 9/17 - 10/7 -.001 .002 -.388 .718 3 weeks (4) - 10/8 - 10/ 28 -.003 .011 -.266 .804 4 weeks (2) - 9/3 - 9/30 .000 .007 -.043 .968 4 weeks (3) - 10/1 - 10/ 28 -.002 .003 -.512 .636 4 weeks (3) - 10/1 - 10/ 28 -.003 .011 -.254 .812 5 weeks (1) - 8/6 - 9/9 -.001 .005 -.147 .891 6 weeks (1) - 8/6 - 9/16 -.002 .010 -.228 .831 6 weeks (2) - 9/17 - 10/28 -.001 .005 -.230 .829 7 weeks - 8/6 - 9/23 -.002 .009 -.216 .840 8 weeks - 8/6 - 9/30 -.002 .009 -.192 .857 9 weeks - 8/6 - 10/7 -.002 .008 -.234 .827 9 weeks - 8/6 - 10/7 -.002 .007 -.238 .824 11 weeks - 8/6 - 10/21 -.002 .007 -.247 .817 12 weeks - 8/6 - 10/28 -.002 .006 -.257 .810 13 weeks - 8/6 - 11/4 -.002 .006 -.267 .803
179 2009 CAUV3 Seasonal Regressions
Variable B Std. Error T Sig. 1 week (1) - 8/6 - 8/12 -.004 .013 -.294 .783 1 week (2) - 8/13 - 8/19 -.005 .014 -.358 .738 1 week (3) - 8/20 - 8/26 -.004 .016 -.244 .819 1 week (4) - 8/27 - 9/2 .000 .010 .028 .979 1 week (5) - 9/3 -9/9 -.003 .009 -.327 .760 1 week (6) - 9/10 - 9/16 -.001 .004 -.134 .900 1 week (7) - 9/17 - 9/23 -.003 .005 -.521 .630 1 week (8) - 9/24 - 9/30 -.002 .012 -.191 .858 1 week (9) - 10/1 - 10/7 -.004 .007 -.609 .575 1 week (10) - 10/8 - 10/14 -.001 .002 -.831 .453 1 week (11) - 10/15 - 10/21 -.001 .001 -.827 .454 1 week (12) - 10/22 - 10/28 -.003 .004 -.699 .523 1 week (13) - 10/29 - 11/4 -.003 .005 -.725 .508 2 weeks (1) - 8/6 - 8/19 -.004 .012 -.351 .743 2 weeks (2) - 8/20 - 9/2 -.002 .013 -.141 .894 2 weeks (3) - 9/3 - 9/16 -.002 .006 -.271 .800 2 weeks (4) - 9/17 - 9/30 -.003 .008 -.305 .775 2 weeks (5) - 10/1 - 10/14 -.003 .004 -.670 .539 2 weeks (6) - 10/15 - 10/28 -.002 .002 -.743 .499 3 weeks (1) - 8/6 - 8/26 -.004 .013 -.325 .761 3 weeks (2) - 8/27 - 9/16 -.001 .007 -.143 .893 3 weeks (3) - 9/17 - 10/7 -.003 .008 -.403 .707 3 weeks (3) - 9/17 - 10/7 -.002 .002 -.779 .479 3 weeks (4) - 10/8 - 10/ 28 -.003 .012 -.257 .810 4 weeks (2) - 9/3 - 9/30 -.002 .007 -.312 .770 4 weeks (3) - 10/1 - 10/ 28 -.002 .003 -.718 .512 4 weeks (3) - 10/1 - 10/ 28 -.003 .011 -.269 .802 5 weeks (1) - 8/6 - 9/9 -.002 .005 -.403 .708 6 weeks (1) - 8/6 - 9/16 -.003 .010 -.261 .807 6 weeks (2) - 9/17 - 10/28 -.002 .005 -.494 .647 7 weeks - 8/6 - 9/23 -.003 .009 -.287 .788 8 weeks - 8/6 - 9/30 -.003 .009 -.289 .787 9 weeks - 8/6 - 10/7 -.003 .008 -.327 .760 9 weeks - 8/6 - 10/7 -.003 .008 -.340 .751 11 weeks - 8/6 - 10/21 -.002 .007 -.350 .744 12 weeks - 8/6 - 10/28 -.002 .007 -.377 .726 13 weeks - 8/6 - 11/4 -.003 .006 -.399 .710
180 2009 SOC0 Seasonal Regressions
Variable B Std. Error t Sig. 1 week (1) - 8/6 - 8/12 -.008 .007 -1.178 .304 1 week (2) - 8/13 - 8/19 -.002 .008 -.200 .851 1 week (3) - 8/20 - 8/26 .002 .009 .224 .834 1 week (4) - 8/27 - 9/2 .000 .006 .015 .989 1 week (5) - 9/3 -9/9 -.002 .005 -.407 .705 1 week (6) - 9/10 - 9/16 -.001 .002 -.540 .618 1 week (7) - 9/17 - 9/23 -.003 .003 -1.047 .354 1 week (8) - 9/24 - 9/30 .003 .007 .394 .714 1 week (9) - 10/1 - 10/7 .002 .004 .484 .654 1 week (10) - 10/8 - 10/14 .000 .001 -.295 .783 1 week (11) - 10/15 - 10/21 .000 .001 .083 .938 1 week (12) - 10/22 - 10/28 .000 .002 -.201 .850 1 week (13) - 10/29 - 11/4 -.001 .003 -.199 .852 2 weeks (1) - 8/6 - 8/19 -.005 .007 -.676 .536 2 weeks (2) - 8/20 - 9/2 .001 .007 .145 .892 2 weeks (3) - 9/3 - 9/16 -.002 .004 -.452 .675 2 weeks (4) - 9/17 - 9/30 .000 .005 -.008 .994 2 weeks (5) - 10/1 - 10/14 .001 .002 .336 .754 2 weeks (6) - 10/15 - 10/28 .000 .001 -.146 .891 3 weeks (1) - 8/6 - 8/26 -.002 .007 -.329 .758 3 weeks (2) - 8/27 - 9/16 -.001 .004 -.249 .815 3 weeks (3) - 9/17 - 10/7 .001 .004 .138 .897 3 weeks (3) - 9/17 - 10/7 .000 .001 -.185 .862 3 weeks (4) - 10/8 - 10/ 28 -.002 .007 -.263 .806 4 weeks (2) - 9/3 - 9/30 -.001 .004 -.212 .842 4 weeks (3) - 10/1 - 10/ 28 .000 .002 .156 .883 4 weeks (3) - 10/1 - 10/ 28 -.002 .007 -.286 .789 5 weeks (1) - 8/6 - 9/9 .000 .003 .024 .982 6 weeks (1) - 8/6 - 9/16 -.002 .006 -.304 .777 6 weeks (2) - 9/17 - 10/28 .000 .003 .066 .950 7 weeks - 8/6 - 9/23 -.002 .005 -.363 .735 8 weeks - 8/6 - 9/30 -.001 .005 -.255 .811 9 weeks - 8/6 - 10/7 -.001 .005 -.196 .854 9 weeks - 8/6 - 10/7 -.001 .005 -.200 .852 11 weeks - 8/6 - 10/21 -.001 .004 -.198 .853 12 weeks - 8/6 - 10/28 -.001 .004 -.203 .849 13 weeks - 8/6 - 11/4 -.001 .004 -.205 .848
181 2009 SOC3 Seasonal Regressions
Variable B Std. Error T Sig. 1 week (1) - 8/6 - 8/12 -.008 .009 -.906 .416 1 week (2) - 8/13 - 8/19 .001 .010 .096 .928 1 week (3) - 8/20 - 8/26 .006 .011 .589 .588 1 week (4) - 8/27 - 9/2 .003 .007 .433 .688 1 week (5) - 9/3 -9/9 .000 .007 -.027 .980 1 week (6) - 9/10 - 9/16 .000 .003 -.064 .952 1 week (7) - 9/17 - 9/23 -.002 .004 -.510 .637 1 week (8) - 9/24 - 9/30 .007 .008 .824 .456 1 week (9) - 10/1 - 10/7 .004 .005 .765 .487 1 week (10) - 10/8 - 10/14 .000 .001 .040 .970 1 week (11) - 10/15 - 10/21 .000 .001 .133 .901 1 week (12) - 10/22 - 10/28 .000 .003 .066 .951 1 week (13) - 10/29 - 11/4 .001 .004 .169 .874 2 weeks (1) - 8/6 - 8/19 -.003 .009 -.386 .719 2 weeks (2) - 8/20 - 9/2 .005 .009 .534 .622 2 weeks (3) - 9/3 - 9/16 .000 .005 -.039 .971 2 weeks (4) - 9/17 - 9/30 .002 .006 .418 .697 2 weeks (5) - 10/1 - 10/14 .002 .003 .629 .563 2 weeks (6) - 10/15 - 10/28 .000 .002 .081 .939 3 weeks (1) - 8/6 - 8/26 .000 .009 -.018 .986 3 weeks (2) - 8/27 - 9/16 .001 .005 .161 .880 3 weeks (3) - 9/17 - 10/7 .003 .005 .531 .624 3 weeks (3) - 9/17 - 10/7 .000 .002 .072 .946 3 weeks (4) - 10/8 - 10/ 28 .001 .009 .070 .948 4 weeks (2) - 9/3 - 9/30 .001 .005 .231 .828 4 weeks (3) - 10/1 - 10/ 28 .001 .002 .425 .693 4 weeks (3) - 10/1 - 10/ 28 .000 .008 .054 .959 5 weeks (1) - 8/6 - 9/9 .002 .004 .430 .689 6 weeks (1) - 8/6 - 9/16 .000 .007 .047 .965 6 weeks (2) - 9/17 - 10/28 .001 .003 .432 .688 7 weeks - 8/6 - 9/23 .000 .007 .004 .997 8 weeks - 8/6 - 9/30 .001 .007 .134 .900 9 weeks - 8/6 - 10/7 .001 .006 .192 .857 9 weeks - 8/6 - 10/7 .001 .006 .190 .859 11 weeks - 8/6 - 10/21 .001 .005 .191 .858 12 weeks - 8/6 - 10/28 .001 .005 .190 .859 13 weeks - 8/6 - 11/4 .001 .005 .190 .858
182 2009 MKT0 Seasonal Regressions
Variables B Std. Error t Sig. 1 week (1) - 8/6 - 8/12 -.012 .012 -1.057 .350 1 week (2) - 8/13 - 8/19 .003 .014 .217 .839 1 week (3) - 8/20 - 8/26 .012 .014 .837 .449 1 week (4) - 8/27 - 9/2 .005 .009 .561 .605 1 week (5) - 9/3 -9/9 .001 .009 .144 .893 1 week (6) - 9/10 - 9/16 .001 .004 .169 .874 1 week (7) - 9/17 - 9/23 .000 .005 -.075 .944 1 week (8) - 9/24 - 9/30 .015 .010 1.545 .197 1 week (9) - 10/1 - 10/7 .009 .006 1.550 .196 1 week (10) - 10/8 - 10/14 .001 .002 .735 .503 1 week (11) - 10/15 - 10/21 .001 .001 .716 .514 1 week (12) - 10/22 - 10/28 .003 .004 .735 .503 1 week (13) - 10/29 - 11/4 .004 .005 .782 .478 2 weeks (1) - 8/6 - 8/19 -.005 .012 -.378 .725 2 weeks (2) - 8/20 - 9/2 .009 .012 .736 .503 2 weeks (3) - 9/3 - 9/16 .001 .006 .153 .886 2 weeks (4) - 9/17 - 9/30 .007 .007 .987 .380 2 weeks (5) - 10/1 - 10/14 .005 .003 1.410 .231 2 weeks (6) - 10/15 - 10/28 .002 .002 .750 .495 3 weeks (1) - 8/6 - 8/26 .001 .013 .071 .947 3 weeks (2) - 8/27 - 9/16 .002 .007 .325 .762 3 weeks (3) - 9/17 - 10/7 .008 .007 1.172 .306 3 weeks (3) - 9/17 - 10/7 .002 .002 .760 .489 3 weeks (4) - 10/8 - 10/ 28 .002 .012 .166 .877 4 weeks (2) - 9/3 - 9/30 .004 .006 .637 .559 4 weeks (3) - 10/1 - 10/ 28 .003 .003 1.168 .308 4 weeks (3) - 10/1 - 10/ 28 .002 .011 .163 .879 5 weeks (1) - 8/6 - 9/9 .005 .005 1.019 .366 6 weeks (1) - 8/6 - 9/16 .002 .010 .164 .878 6 weeks (2) - 9/17 - 10/28 .005 .004 1.099 .333 7 weeks - 8/6 - 9/23 .001 .009 .147 .890 8 weeks - 8/6 - 9/30 .003 .009 .345 .748 9 weeks - 8/6 - 10/7 .004 .008 .442 .681 9 weeks - 8/6 - 10/7 .003 .008 .452 .675 11 weeks – 8/6 - 10/21 .003 .007 .461 .669 12 weeks – 8/6 - 10/28 .003 .006 .487 .652 13 weeks – 8/6 - 11/4 .003 .006 .508 .638
183 2009 MKT3 Seasonal Regressions
Variables B Std. Error t Sig. 1 week (1) - 8/6 - 8/12 -.008 .013 -.629 .564 1 week (2) - 8/13 - 8/19 .008 .014 .563 .603 1 week (3) - 8/20 - 8/26 .017 .014 1.240 .283 1 week (4) - 8/27 - 9/2 .008 .009 .887 .425 1 week (5) - 9/3 -9/9 .004 .009 .485 .653 1 week (6) - 9/10 - 9/16 .002 .004 .415 .700 1 week (7) - 9/17 - 9/23 .001 .005 .114 .914 1 week (8) - 9/24 - 9/30 .016 .010 1.683 .168 1 week (9) - 10/1 - 10/7 .010 .005 1.763 .153 1 week (10) - 10/8 - 10/14 .001 .002 .835 .451 1 week (11) - 10/15 - 10/21 .001 .001 .628 .564 1 week (12) - 10/22 - 10/28 .003 .004 .716 .514 1 week (13) - 10/29 - 11/4 .005 .005 .989 .379 2 weeks (1) - 8/6 - 8/19 .000 .013 -.017 .987 2 weeks (2) - 8/20 - 9/2 .012 .011 1.110 .329 2 weeks (3) - 9/3 - 9/16 .003 .006 .469 .663 2 weeks (4) - 9/17 - 9/30 .008 .007 1.144 .316 2 weeks (5) - 10/1 - 10/14 .005 .003 1.600 .185 2 weeks (6) - 10/15 - 10/28 .002 .002 .715 .514 3 weeks (1) - 8/6 - 8/26 .005 .013 .422 .695 3 weeks (2) - 8/27 - 9/16 .005 .007 .644 .555 3 weeks (3) - 9/17 - 10/7 .009 .007 1.351 .248 3 weeks (3) - 9/17 - 10/7 .002 .002 .759 .490 3 weeks (4) - 10/8 - 10/ 28 .006 .012 .513 .635 4 weeks (2) - 9/3 - 9/30 .006 .006 .893 .422 4 weeks (3) - 10/1 - 10/ 28 .004 .003 1.250 .280 4 weeks (3) - 10/1 - 10/ 28 .006 .011 .510 .637 5 weeks (1) - 8/6 - 9/9 .006 .005 1.211 .293 6 weeks (1) - 8/6 - 9/16 .005 .010 .507 .639 6 weeks (2) - 9/17 - 10/28 .005 .004 1.231 .286 7 weeks - 8/6 - 9/23 .004 .009 .483 .654 8 weeks - 8/6 - 9/30 .006 .009 .674 .537 9 weeks - 8/6 - 10/7 .006 .008 .775 .482 9 weeks - 8/6 - 10/7 .006 .007 .783 .477 11 weeks – 8/6 - 10/21 .005 .007 .791 .473 12 weeks – 8/6 - 10/28 .005 .006 .811 .463 13 weeks – 8/6 - 11/4 .005 .006 .829 .454
184 2010 AHI0 Seasonal Regressions
Variable B Std. Error t Sig. week 1 (1) - 4/24-4/30 .029 .016 1.771 .151 week 1 (2) - 5/1-5/7 .020 .008 2.558 .063 week 1 (3) - 5/8-5/14 -.003 .020 -.172 .872 week 1 (4) - 5/15-5/21 -.008 .004 -1.808 .145 week 1 (5) - 5/22-5/28 -.005 .012 -.387 .718 week 1 (6) - 5/29-6/4 -.009 .005 -1.686 .234 week 1 (7) - 6/5-6/11 -.005 .014 -.397 .730 week 1 (8) - 6/12-6/18 -.013 .003 -4.776 .041 week 1 (9) - 6/19-6/25 -.020 .005 -3.895 .060 week 1 (10) - 6/26-7/2 -.016 .001 -12.046 .007 week 1 (11) - 7/3-7/9 -.009 .014 -.614 .602 week 1 (12) - 7/10-7/16 -.022 .041 -.538 .644 week 1 (13) - 7/17-7/23 -.025 .032 -.790 .513 week 1 (14) - 7/17-7/23 -.029 .005 -6.195 .025 week 1 (15) - 7/31-8/6 -.029 .006 -5.023 .125 week 1 (16) - 8/7-8/13 -.017 .016 -1.064 .480 week 1 (17) - 8/14-8/20 -.021 .036 -.584 .663 week 1 (18) - 8/21-8/27 -.006 .028 -.195 .877 week 1 (19) - 8/28-9/3 -.009 .015 -.616 .649 week 1 (20) - 9/4-9-10 -.008 .002 -4.622 .136 week 1 (21) - 9/11-9/17 -.004 .009 -.410 .752 week 1 (22) - 9/18-9/24 -.002 .010 -.164 .897 week 2 (1) - 4/24-5/7 .024 .010 2.291 .084 week 2 (2) - 5/8-5/21 -.006 .010 -.584 .591 week 2 (3) - 5/22-6/4 -.010 .006 -1.774 .218 week 2 (4) - 6/5-6/18 -.009 .008 -1.183 .358 week 2 (5) - 6/19-7/2 -.018 .003 -5.540 .031 week 2 (6) - 7/3-7/16 -.015 .027 -.564 .630 week 2 (7) - 7/17-7/30 -.027 .015 -1.762 .220 week 2 (8) - 7/31-8/13 -.023 .011 -2.075 .286 week 2 (9) - 8/14-8/27 -.013 .033 -.411 .752 week 2 (10) - 8/28-9/10 -.008 .007 -1.246 .431 week 2 (11) - 9/11-9/24 -.002 .009 -.260 .838 week 3 (1) - 4/24-5/14 .030 .013 2.227 .090 week 3 (2) - 5/15-6/4 -.001 .004 -.134 .905 week 3 (3) - 6/5-6/25 -.009 .003 -3.160 .087 week 3 (4) - 6/26-7/16 -.016 .003 -5.077 .037 week 3 (5) - 7/17-8/6 -.024 .007 -3.395 .182 week 3 (6) - 8/7-8/27 -.024 .006 -4.431 .141 Continued on page 186
185 Continued from page 185
Variable B Std. Error t Sig. week 3 (7) - 8/28-9/17 -.007 .006 -1.217 .438 week 4 (1) - 4/24-5/21 .009 .006 1.636 .177 week 4 (2) - 5/22-6/18 -.009 .002 -5.810 .028 week 4 (3) - 6/19-7/16 -.016 .015 -1.092 .389 week 4 (4) - 7/17-8/13 -.025 .005 -5.308 .119 week 4 (5) - 8/14-9/10 -.011 .020 -.533 .688 week 5 (1) - 4/24-5/28 .003 .008 .341 .766 week 5 (2) - 5/29-7/2 -.013 .003 -4.540 .045 week 5 (3) - 7/3-8/6 -.023 .019 -1.220 .437 week 5 (4) - 8/7-9/10 -.012 .019 -.644 .636 week 6 (1) - 4/24-6/4 .013 .012 1.047 .405 week 6 (2) - 6/5-7/16 -.012 .002 -7.841 .016 week 6 (3) - 7/17-8/27 -.018 .016 -1.151 .455 week 7 (1) - 4/24-6/11 .000 .004 -.050 .964 week 7 (2) - 6/12-7/30 -.019 .013 -1.459 .282 week 7 (3) - 7/31-9/17 -.013 .015 -.868 .545 week 8 (1) - 4/24-6/18 -.002 .003 -.507 .663 week 8 (2) - 6/19-8/13 -.023 .007 -3.234 .191 week 9 (1) - 4/24-6/25 -.004 .003 -1.236 .342 week 9 (2) - 6/26-8/27 -.020 .001 -13.474 .047 week 10 (1) - 4/24-7/2 -.005 .003 -1.456 .283 week 10 (2) - 7/3-9/10 -.018 .000 -526.058 .001 week 11 (1) - 4/24-7/9 -.005 .004 -1.440 .286 week 11 (2) - 7/10-9/24 -.016 .003 -5.076 .124 week 12 (1) - 4/24-7/16 .000 .007 .049 .966 week 13 - 4/24-7/23 -.008 .009 -.922 .454 week 14 - 4/26-7/30 -.010 .008 -1.216 .348 week 15 - 4/24-8/6 -.011 .009 -1.252 .429 week 16 - 4/26-8/13 -.012 .001 -9.507 .067 week 17 - 4/26-8/13 -.012 .008 -1.409 .393 week 18 - 4/24-8/27 -.011 .004 -2.537 .239 week 19 - 4/24-9/3 -.012 .003 -4.443 .141 week 20 - 4/24-9/10 -.011 .002 -4.823 .130 week 21 - 4/26-9/17 -.011 .002 -5.308 .119 week 22 - 4/24-9/24 -.011 .001 -8.145 .078
186 2010 AHI3 Seasonal Regressions
Variable B Std. Error t Sig. week 1 (1) - 4/24-4/30 .029 .019 1.560 .194 week 1 (2) - 5/1-5/7 .021 .009 2.273 .085 week 1 (3) - 5/8-5/14 .000 .022 -.004 .997 week 1 (4) - 5/15-5/21 -.007 .005 -1.471 .215 week 1 (5) - 5/22-5/28 -.004 .013 -.322 .763 week 1 (6) - 5/29-6/4 -.009 .007 -1.289 .326 week 1 (7) - 6/5-6/11 -.005 .017 -.320 .779 week 1 (8) - 6/12-6/18 -.016 .003 -5.436 .032 week 1 (9) - 6/19-6/25 -.026 .005 -5.498 .032 week 1 (10) - 6/26-7/2 -.019 .001 -15.934 .004 week 1 (11) - 7/3-7/9 -.012 .017 -.683 .565 week 1 (12) - 7/10-7/16 -.033 .047 -.707 .553 week 1 (13) - 7/17-7/23 -.036 .037 -.986 .428 week 1 (14) - 7/17-7/23 -.035 .006 -6.068 .026 week 1 (15) - 7/31-8/6 -.034 .012 -2.862 .214 week 1 (16) - 8/7-8/13 -.018 .022 -.803 .569 week 1 (17) - 8/14-8/20 -.019 .047 -.410 .752 week 1 (18) - 8/21-8/27 -.002 .035 -.053 .966 week 1 (19) - 8/28-9/3 -.009 .020 -.438 .737 week 1 (20) - 9/4-9-10 -.009 .001 -13.584 .047 week 1 (21) - 9/11-9/17 -.003 .011 -.255 .841 week 1 (22) - 9/18-9/24 .000 .012 -.023 .985 week 2 (1) - 4/24-5/7 .025 .012 2.003 .116 week 2 (2) - 5/8-5/21 -.004 .011 -.347 .746 week 2 (3) - 5/22-6/4 -.013 .006 -2.064 .175 week 2 (4) - 6/5-6/18 -.010 .010 -1.089 .390 week 2 (5) - 6/19-7/2 -.022 .002 -9.284 .011 week 2 (6) - 7/3-7/16 -.022 .031 -.711 .551 week 2 (7) - 7/17-7/30 -.035 .016 -2.218 .157 week 2 (8) - 7/31-8/13 -.026 .017 -1.498 .375 week 2 (9) - 8/14-8/27 -.011 .042 -.256 .840 week 2 (10) - 8/28-9/10 -.009 .009 -.941 .519 week 2 (11) - 9/11-9/24 -.001 .011 -.116 .927 week 3 (1) - 4/24-5/14 .031 .016 1.942 .124 week 3 (2) - 5/15-6/4 -.001 .005 -.223 .844 week 3 (3) - 6/5-6/25 -.011 .003 -3.769 .064 week 3 (4) - 6/26-7/16 -.020 .003 -5.912 .027 week 3 (5) - 7/17-8/6 -.027 .012 -2.203 .271 week 3 (6) - 8/7-8/27 -.029 .011 -2.645 .230 Continued on page 188
187 2010 AHI3 Seasonal Regressions : Continued Variable B Std. Error t Sig. week 3 (7) - 8/28-9/17 -.010 .006 -1.637 .349 week 4 (1) - 4/24-5/21 .010 .006 1.717 .161 week 4 (2) - 5/22-6/18 -.012 .002 -6.384 .024 week 4 (3) - 6/19-7/16 -.022 .017 -1.299 .324 week 4 (4) - 7/17-8/13 -.031 .001 -21.426 .030 week 4 (5) - 8/14-9/10 -.009 .026 -.365 .777 week 5 (1) - 4/24-5/28 .001 .009 .155 .891 week 5 (2) - 5/29-7/2 -.015 .004 -3.756 .064 week 5 (3) - 7/3-8/6 -.031 .019 -1.642 .348 week 5 (4) - 8/7-9/10 -.011 .025 -.462 .724 week 6 (1) - 4/24-6/4 .013 .016 .828 .495 week 6 (2) - 6/5-7/16 -.015 .000 -31.937 .001 week 6 (3) - 7/17-8/27 -.019 .022 -.870 .544 week 7 (1) - 4/24-6/11 -.001 .004 -.253 .824 week 7 (2) - 6/12-7/30 -.025 .014 -1.775 .218 week 7 (3) - 7/31-9/17 -.013 .021 -.649 .634 week 8 (1) - 4/24-6/18 -.003 .004 -.751 .531 week 8 (2) - 6/19-8/13 -.027 .012 -2.127 .280 week 9 (1) - 4/24-6/25 -.006 .003 -1.645 .242 week 9 (2) - 6/26-8/27 -.024 .002 -15.245 .042 week 10 (1) - 4/24-7/2 -.007 .003 -1.947 .191 week 10 (2) - 7/3-9/10 -.021 .003 -7.217 .088 week 11 (1) - 4/24-7/9 -.007 .004 -1.855 .205 week 11 (2) - 7/10-9/24 -.018 .006 -2.881 .213 week 12 (1) - 4/24-7/16 -.001 .008 -.108 .924 week 13 - 4/24-7/23 -.011 .010 -1.162 .365 week 14 - 4/26-7/30 -.013 .009 -1.522 .267 week 15 - 4/24-8/6 -.015 .009 -1.689 .340 week 16 - 4/26-8/13 -.015 .004 -4.010 .156 week 17 - 4/26-8/13 -.015 .008 -1.931 .304 week 18 - 4/24-8/27 -.014 .003 -4.161 .150 week 19 - 4/24-9/3 -.014 .001 -12.199 .052 week 20 - 4/24-9/10 -.014 .001 -15.394 .041 week 21 - 4/26-9/17 -.013 .001 -21.426 .030 week 22 - 4/24-9/24 -.013 .000 -57.385 .011
188 2010 BD0 Seasonal Regressions
Variable B Std. Error t Sig. week 1 (1) - 4/24-4/30 -.014 .012 -1.198 .297 week 1 (2) - 5/1-5/7 -.007 .008 -.869 .434 week 1 (3) - 5/8-5/14 .014 .011 1.335 .253 week 1 (4) - 5/15-5/21 .004 .003 1.276 .271 week 1 (5) - 5/22-5/28 .003 .008 .447 .678 week 1 (6) - 5/29-6/4 .007 .002 4.163 .053 week 1 (7) - 6/5-6/11 .009 .008 1.192 .356 week 1 (8) - 6/12-6/18 .008 .003 2.937 .099 week 1 (9) - 6/19-6/25 .010 .008 1.337 .313 week 1 (10) - 6/26-7/2 .009 .004 2.142 .166 week 1 (11) - 7/3-7/9 .000 .011 -.034 .976 week 1 (12) - 7/10-7/16 -.004 .030 -.147 .896 week 1 (13) - 7/17-7/23 .002 .026 .059 .958 week 1 (14) - 7/17-7/23 .019 .006 3.389 .077 week 1 (15) - 7/31-8/6 .021 .004 4.811 .130 week 1 (16) - 8/7-8/13 .016 .006 2.714 .225 week 1 (17) - 8/14-8/20 .024 .018 1.341 .408 week 1 (18) - 8/21-8/27 .012 .017 .675 .622 week 1 (19) - 8/28-9/3 .010 .007 1.409 .393 week 1 (20) - 9/4-9-10 .005 .003 1.417 .391 week 1 (21) - 9/11-9/17 .005 .005 1.010 .497 week 1 (22) - 9/18-9/24 .004 .006 .632 .641 week 2 (1) - 4/24-5/7 -.010 .009 -1.142 .317 week 2 (2) - 5/8-5/21 .009 .004 2.096 .104 week 2 (3) - 5/22-6/4 .004 .006 .651 .582 week 2 (4) - 6/5-6/18 .009 .003 2.579 .123 week 2 (5) - 6/19-7/2 .010 .006 1.637 .243 week 2 (6) - 7/3-7/16 -.002 .020 -.125 .912 week 2 (7) - 7/17-7/30 .010 .015 .652 .581 week 2 (8) - 7/31-8/13 .019 .001 20.977 .030 week 2 (9) - 8/14-8/27 .018 .018 1.013 .496 week 2 (10) - 8/28-9/10 .007 .002 3.547 .175 week 2 (11) - 9/11-9/24 .004 .005 .770 .582 week 3 (1) - 4/24-5/14 -.012 .012 -.994 .376 week 3 (2) - 5/15-6/4 -.002 .003 -.524 .653 week 3 (3) - 6/5-6/25 .006 .002 2.378 .140 week 3 (4) - 6/26-7/16 .009 .006 1.561 .259 week 3 (5) - 7/17-8/6 .018 .002 8.660 .073 week 3 (6) - 8/7-8/27 .018 .003 5.511 .114 Continued on page 190
189 2010 BD0 Seasonal Regressions: Continued Variable B Std. Error t Sig. week 3 (7) - 8/28-9/17 .003 .006 .522 .694 week 4 (1) - 4/24-5/21 -.001 .005 -.148 .890 week 4 (2) - 5/22-6/18 .006 .002 3.063 .092 week 4 (3) - 6/19-7/16 .004 .013 .271 .812 week 4 (4) - 7/17-8/13 .015 .010 1.501 .374 week 4 (5) - 8/14-9/10 .013 .010 1.237 .433 week 5 (1) - 4/24-5/28 -.005 .004 -1.119 .380 week 5 (2) - 5/29-7/2 .009 .002 5.527 .031 week 5 (3) - 7/3-8/6 .010 .019 .524 .693 week 5 (4) - 8/7-9/10 .013 .009 1.471 .380 week 6 (1) - 4/24-6/4 -.013 .005 -2.799 .107 week 6 (2) - 6/5-7/16 .007 .004 1.991 .185 week 6 (3) - 7/17-8/27 .016 .005 3.079 .200 week 7 (1) - 4/24-6/11 -.001 .002 -.491 .672 week 7 (2) - 6/12-7/30 .006 .012 .490 .673 week 7 (3) - 7/31-9/17 .013 .006 2.047 .289 week 8 (1) - 4/24-6/18 .000 .002 .028 .980 week 8 (2) - 6/19-8/13 .017 .002 9.815 .065 week 9 (1) - 4/24-6/25 .001 .003 .494 .670 week 9 (2) - 6/26-8/27 .013 .006 1.942 .303 week 10 (1) - 4/24-7/2 .002 .003 .618 .600 week 10 (2) - 7/3-9/10 .012 .005 2.343 .257 week 11 (1) - 4/24-7/9 .002 .003 .513 .659 week 11 (2) - 7/10-9/24 .011 .002 4.763 .132 week 12 (1) - 4/24-7/16 -.003 .004 -.767 .523 week 13 - 4/24-7/23 .001 .007 .164 .885 week 14 - 4/26-7/30 .003 .007 .360 .753 week 15 - 4/24-8/6 .005 .009 .540 .685 week 16 - 4/26-8/13 .009 .003 3.272 .189 week 17 - 4/26-8/13 .005 .009 .616 .649 week 18 - 4/24-8/27 .006 .006 1.017 .495 week 19 - 4/24-9/3 .007 .005 1.392 .396 week 20 - 4/24-9/10 .007 .005 1.443 .386 week 21 - 4/26-9/17 .007 .004 1.501 .374 week 22 - 4/24-9/24 .007 .004 1.732 .333
190 2010 BD3 Seasonal Regressions
Variable B Std. Error t Sig. week 1 (1) - 4/24-4/30 -.012 .014 -.880 .429 week 1 (2) - 5/1-5/7 -.009 .008 -1.167 .308 week 1 (3) - 5/8-5/14 .008 .013 .599 .581 week 1 (4) - 5/15-5/21 .006 .003 1.928 .126 week 1 (5) - 5/22-5/28 .006 .008 .736 .503 week 1 (6) - 5/29-6/4 .007 .003 2.936 .099 week 1 (7) - 6/5-6/11 .007 .009 .813 .502 week 1 (8) - 6/12-6/18 .009 .002 4.090 .055 week 1 (9) - 6/19-6/25 .013 .007 1.960 .189 week 1 (10) - 6/26-7/2 .011 .003 3.402 .077 week 1 (11) - 7/3-7/9 .003 .011 .228 .841 week 1 (12) - 7/10-7/16 .004 .032 .122 .914 week 1 (13) - 7/17-7/23 .009 .026 .333 .771 week 1 (14) - 7/17-7/23 .021 .004 5.320 .034 week 1 (15) - 7/31-8/6 .022 .001 21.552 .030 week 1 (16) - 8/7-8/13 .015 .009 1.781 .326 week 1 (17) - 8/14-8/20 .022 .022 .973 .509 week 1 (18) - 8/21-8/27 .009 .019 .465 .723 week 1 (19) - 8/28-9/3 .009 .009 1.019 .494 week 1 (20) - 9/4-9-10 .005 .003 2.039 .290 week 1 (21) - 9/11-9/17 .004 .006 .732 .598 week 1 (22) - 9/18-9/24 .003 .007 .429 .742 week 2 (1) - 4/24-5/7 -.010 .010 -1.034 .360 week 2 (2) - 5/8-5/21 .007 .006 1.163 .309 week 2 (3) - 5/22-6/4 .005 .005 1.017 .416 week 2 (4) - 6/5-6/18 .008 .004 1.856 .205 week 2 (5) - 6/19-7/2 .012 .005 2.442 .135 week 2 (6) - 7/3-7/16 .003 .021 .147 .897 week 2 (7) - 7/17-7/30 .015 .015 1.007 .420 week 2 (8) - 7/31-8/13 .019 .004 4.781 .131 week 2 (9) - 8/14-8/27 .016 .021 .734 .597 week 2 (10) - 8/28-9/10 .007 .003 2.161 .276 week 2 (11) - 9/11-9/24 .003 .006 .543 .683 week 3 (1) - 4/24-5/14 -.012 .013 -.935 .402 week 3 (2) - 5/15-6/4 -.001 .003 -.238 .834 week 3 (3) - 6/5-6/25 .006 .002 2.892 .102 week 3 (4) - 6/26-7/16 .011 .005 2.336 .145 week 3 (5) - 7/17-8/6 .018 .001 22.920 .028 week 3 (6) - 8/7-8/27 .019 .000 47.755 .013 Continued on page 192
191 2010 BD3 Seasonal Regressions: Continued Variable B Std. Error t Sig. week 3 (7) - 8/28-9/17 .004 .006 .744 .593 week 4 (1) - 4/24-5/21 -.002 .005 -.369 .731 week 4 (2) - 5/22-6/18 .007 .001 4.580 .045 week 4 (3) - 6/19-7/16 .007 .013 .566 .628 week 4 (4) - 7/17-8/13 .017 .008 2.186 .273 week 4 (5) - 8/14-9/10 .011 .012 .899 .534 week 5 (1) - 4/24-5/28 -.004 .005 -.775 .519 week 5 (2) - 5/29-7/2 .010 .001 8.521 .013 week 5 (3) - 7/3-8/6 .013 .018 .747 .592 week 5 (4) - 8/7-9/10 .012 .011 1.062 .481 week 6 (1) - 4/24-6/4 -.012 .007 -1.838 .207 week 6 (2) - 6/5-7/16 .008 .003 3.035 .094 week 6 (3) - 7/17-8/27 .016 .008 1.956 .301 week 7 (1) - 4/24-6/11 -.001 .003 -.280 .806 week 7 (2) - 6/12-7/30 .010 .012 .816 .500 week 7 (3) - 7/31-9/17 .012 .009 1.422 .390 week 8 (1) - 4/24-6/18 .000 .002 .203 .858 week 8 (2) - 6/19-8/13 .018 .001 17.516 .036 week 9 (1) - 4/24-6/25 .002 .003 .750 .532 week 9 (2) - 6/26-8/27 .014 .005 3.049 .202 week 10 (1) - 4/24-7/2 .003 .003 .900 .463 week 10 (2) - 7/3-9/10 .013 .003 4.003 .156 week 11 (1) - 4/24-7/9 .003 .003 .822 .497 week 11 (2) - 7/10-9/24 .012 .001 20.655 .031 week 12 (1) - 4/24-7/16 -.002 .005 -.455 .694 week 13 - 4/24-7/23 .003 .007 .440 .703 week 14 - 4/26-7/30 .005 .007 .657 .579 week 15 - 4/24-8/6 .006 .008 .766 .584 week 16 - 4/26-8/13 .009 .001 7.196 .088 week 17 - 4/26-8/13 .007 .008 .860 .548 week 18 - 4/24-8/27 .007 .005 1.406 .394 week 19 - 4/24-9/3 .008 .004 1.997 .296 week 20 - 4/24-9/10 .008 .004 2.084 .285 week 21 - 4/26-9/17 .007 .003 2.186 .273 week 22 - 4/24-9/24 .007 .003 2.617 .232
192 2010 SOIL0 Seasonal Regressions
Variable B Std. Error t Sig. Week 1 (1) - 4/24-4/30 .010 .013 .728 .507 Week 1 (2) - 5/1-5/7 .013 .005 2.742 .052 Week 1 (3) - 5/8-5/14 .013 .011 1.114 .328 Week 1 (4) - 5/15-5/21 -.003 .003 -.873 .432 Week 1 (5) - 5/22-5/28 .001 .008 .133 .901 Week 1 (6) - 5/29-6/4 -.007 .005 -1.421 .291 Week 1 (7) - 6/5-6/11 .002 .013 .142 .900 Week 1 (8) - 6/12-6/18 -.006 .007 -.864 .479 Week 1 (9) - 6/19-6/25 -.013 .010 -1.296 .324 Week 1 (10) - 6/26-7/2 -.010 .007 -1.489 .275 Week 1 (11) - 7/3-7/9 -.012 .011 -1.044 .406 Week 1 (12) - 7/10-7/16 -.016 .037 -.421 .715 Week 1 (13) - 7/17-7/23 -.017 .030 -.574 .624 Week 1 (14) - 7/17-7/23 -.015 .015 -1.016 .417 Week 1 (15) - 7/31-8/6 -.032 .007 -4.811 .130 Week 1 (16) - 8/7-8/13 -.024 .009 -2.714 .225 Week 1 (17) - 8/14-8/20 -.037 .027 -1.341 .408 Week 1 (18) - 8/21-8/27 -.018 .026 -.675 .622 Week 1 (19) - 8/28-9/3 -.016 .011 -1.409 .393 Week 1 (20) - 9/4-9-10 -.007 .005 -1.417 .391 Week 1 (21) - 9/11-9/17 -.007 .007 -1.010 .497 Week 1 (22) - 9/18-9/24 -.006 .009 -.632 .641 Week 2 (1) - 4/24-5/7 .011 .009 1.310 .260 Week 2 (2) - 5/8-5/21 .005 .006 .799 .469 Week 2 (3) - 5/22-6/4 -.007 .006 -1.261 .335 Week 2 (4) - 6/5-6/18 -.002 .009 -.264 .817 Week 2 (5) - 6/19-7/2 -.011 .008 -1.333 .314 Week 2 (6) - 7/3-7/16 -.013 .024 -.562 .631 Week 2 (7) - 7/17-7/30 -.016 .018 -.894 .466 Week 2 (8) - 7/31-8/13 -.028 .001 -20.977 .030 Week 2 (9) - 8/14-8/27 -.027 .027 -1.013 .496 Week 2 (10) - 8/28-9/10 -.011 .003 -3.547 .175 Week 2 (11) - 9/11-9/24 -.006 .008 -.770 .582 Week 3 (1) - 4/24-5/14 .017 .010 1.654 .174 Week 3 (2) - 5/15-6/4 -.002 .004 -.471 .684 Week 3 (3) - 6/5-6/25 -.003 .005 -.616 .601 Week 3 (4) - 6/26-7/16 -.011 .007 -1.671 .237 Week 3 (5) - 7/17-8/6 -.027 .003 -8.660 .073 Week 3 (6) - 8/7-8/27 -.027 .005 -5.511 .114 Continued on page 194
193 2010 SOIL0 Seasonal Regressions: Continued Variable B Std. Error t Sig. Week 3 (7) - 8/28-9/17 -.005 .009 -.522 .694 Week 4 (1) - 4/24-5/21 .008 .003 3.164 .034 Week 4 (2) - 5/22-6/18 -.005 .005 -.963 .437 Week 4 (3) - 6/19-7/16 -.012 .014 -.870 .476 Week 4 (4) - 7/17-8/13 -.023 .015 -1.501 .374 Week 4 (5) - 8/14-9/10 -.019 .015 -1.237 .433 Week 5 (1) - 4/24-5/28 .003 .006 .515 .658 Week 5 (2) - 5/29-7/2 -.007 .007 -1.029 .412 Week 5 (3) - 7/3-8/6 -.015 .029 -.524 .693 Week 5 (4) - 8/7-9/10 -.020 .014 -1.471 .380 Week 6 (1) - 4/24-6/4 .008 .012 .675 .569 Week 6 (2) - 6/5-7/16 -.007 .006 -1.242 .340 Week 6 (3) - 7/17-8/27 -.024 .008 -3.079 .200 Week 7 (1) - 4/24-6/11 .002 .003 .493 .671 Week 7 (2) - 6/12-7/30 -.012 .014 -.907 .460 Week 7 (3) - 7/31-9/17 -.020 .010 -2.047 .289 Week 8 (1) - 4/24-6/18 .001 .003 .268 .814 Week 8 (2) - 6/19-8/13 -.026 .003 -9.815 .065 Week 9 (1) - 4/24-6/25 -.001 .004 -.275 .809 Week 9 (2) - 6/26-8/27 -.019 .010 -1.942 .303 Week 10 (1) - 4/24-7/2 -.002 .004 -.379 .741 Week 10 (2) - 7/3-9/10 -.018 .008 -2.343 .257 Week 11 (1) - 4/24-7/9 -.003 .004 -.620 .598 Week 11 (2) - 7/10-9/24 -.017 .004 -4.763 .132 Week 12 (1) - 4/24-7/16 .001 .006 .087 .939 Week 13 - 4/24-7/23 -.005 .009 -.539 .644 Week 14 - 4/26-7/30 -.006 .008 -.666 .574 Week 15 - 4/24-8/6 -.007 .014 -.540 .685 Week 16 - 4/26-8/13 -.013 .004 -3.272 .189 Week 17 - 4/26-8/13 -.008 .013 -.616 .649 Week 18 - 4/24-8/27 -.009 .009 -1.017 .495 Week 19 - 4/24-9/3 -.010 .008 -1.392 .396 Week 20 - 4/24-9/10 -.010 .007 -1.443 .386 Week 21 - 4/26-9/17 -.010 .007 -1.501 .374 Week 22 - 4/24-9/24 -.010 .006 -1.732 .333
194 2010 SOIL3 Seasonal Regressions
Variable B Std. Error t Sig. Week 1 (1) - 4/24-4/30 .007 .027 .257 .810 Week 1 (2) - 5/1-5/7 .020 .012 1.639 .177 Week 1 (3) - 5/8-5/14 .030 .020 1.509 .206 Week 1 (4) - 5/15-5/21 -.008 .006 -1.351 .248 Week 1 (5) - 5/22-5/28 -.006 .015 -.425 .693 Week 1 (6) - 5/29-6/4 -.007 .011 -.641 .587 Week 1 (7) - 6/5-6/11 .013 .020 .667 .573 Week 1 (8) - 6/12-6/18 -.009 .013 -.728 .542 Week 1 (9) - 6/19-6/25 -.026 .015 -1.666 .238 Week 1 (10) - 6/26-7/2 -.018 .012 -1.462 .281 Week 1 (11) - 7/3-7/9 -.030 .012 -2.423 .136 Week 1 (12) - 7/10-7/16 -.061 .052 -1.165 .364 Week 1 (13) - 7/17-7/23 -.056 .041 -1.371 .304 Week 1 (14) - 7/17-7/23 -.023 .028 -.827 .495 Week 1 (15) - 7/31-8/6 -.064 .019 -3.414 .181 Week 1 (16) - 8/7-8/13 -.035 .039 -.891 .537 Week 1 (17) - 8/14-8/20 -.040 .084 -.471 .720 Week 1 (18) - 8/21-8/27 -.007 .064 -.104 .934 Week 1 (19) - 8/28-9/3 -.017 .035 -.500 .705 Week 1 (20) - 9/4-9-10 -.017 .002 -7.979 .079 Week 1 (21) - 9/11-9/17 -.006 .020 -.310 .809 Week 1 (22) - 9/18-9/24 -.002 .022 -.074 .953 Week 2 (1) - 4/24-5/7 .013 .019 .705 .520 Week 2 (2) - 5/8-5/21 .011 .011 .976 .384 Week 2 (3) - 5/22-6/4 -.017 .007 -2.359 .142 Week 2 (4) - 6/5-6/18 .002 .016 .109 .923 Week 2 (5) - 6/19-7/2 -.021 .014 -1.527 .266 Week 2 (6) - 7/3-7/16 -.045 .032 -1.408 .294 Week 2 (7) - 7/17-7/30 -.040 .025 -1.577 .255 Week 2 (8) - 7/31-8/13 -.049 .029 -1.678 .342 Week 2 (9) - 8/14-8/27 -.023 .075 -.311 .808 Week 2 (10) - 8/28-9/10 -.017 .016 -1.042 .487 Week 2 (11) - 9/11-9/24 -.003 .020 -.168 .894 Week 3 (1) - 4/24-5/14 .021 .023 .916 .411 Week 3 (2) - 5/15-6/4 -.007 .005 -1.230 .344 Week 3 (3) - 6/5-6/25 -.005 .010 -.532 .648 Week 3 (4) - 6/26-7/16 -.021 .011 -1.945 .191 Week 3 (5) - 7/17-8/6 -.051 .020 -2.541 .239 Week 3 (6) - 8/7-8/27 -.053 .017 -3.118 .198 Continued on page 196
195 2010 SOIL3 Seasonal Regressions: Continued Variable B Std. Error t Sig. Week 3 (7) - 8/28-9/17 -.018 .012 -1.463 .382 Week 4 (1) - 4/24-5/21 .012 .007 1.728 .159 Week 4 (2) - 5/22-6/18 -.008 .009 -.812 .502 Week 4 (3) - 6/19-7/16 -.033 .018 -1.863 .203 Week 4 (4) - 7/17-8/13 -.057 .006 -10.189 .062 Week 4 (5) - 8/14-9/10 -.020 .046 -.424 .745 Week 5 (1) - 4/24-5/28 -.001 .012 -.085 .940 Week 5 (2) - 5/29-7/2 -.009 .013 -.714 .549 Week 5 (3) - 7/3-8/6 -.055 .037 -1.467 .381 Week 5 (4) - 8/7-9/10 -.023 .044 -.526 .692 Week 6 (1) - 4/24-6/4 .003 .023 .113 .920 Week 6 (2) - 6/5-7/16 -.013 .010 -1.286 .327 Week 6 (3) - 7/17-8/27 -.036 .038 -.964 .512 Week 7 (1) - 4/24-6/11 .000 .006 -.058 .959 Week 7 (2) - 6/12-7/30 -.032 .018 -1.749 .222 Week 7 (3) - 7/31-9/17 -.026 .037 -.724 .601 Week 8 (1) - 4/24-6/18 -.001 .005 -.157 .890 Week 8 (2) - 6/19-8/13 -.049 .020 -2.445 .247 Week 9 (1) - 4/24-6/25 -.004 .006 -.670 .572 Week 9 (2) - 6/26-8/27 -.043 .001 -69.877 .009 Week 10 (1) - 4/24-7/2 -.005 .006 -.761 .526 Week 10 (2) - 7/3-9/10 -.039 .003 -11.533 .055 Week 11 (1) - 4/24-7/9 -.007 .006 -1.191 .356 Week 11 (2) - 7/10-9/24 -.034 .010 -3.440 .180 Week 12 (1) - 4/24-7/16 -.005 .010 -.513 .659 Week 13 - 4/24-7/23 -.015 .012 -1.255 .336 Week 14 - 4/26-7/30 -.016 .012 -1.373 .303 Week 15 - 4/24-8/6 -.026 .018 -1.507 .373 Week 16 - 4/26-8/13 -.027 .005 -5.112 .123 Week 17 - 4/26-8/13 -.027 .016 -1.711 .337 Week 18 - 4/24-8/27 -.026 .008 -3.388 .183 Week 19 - 4/24-9/3 -.026 .003 -7.475 .085 Week 20 - 4/24-9/10 -.025 .003 -8.578 .074 Week 21 - 4/26-9/17 -.024 .002 -10.189 .062 Week 22 - 4/24-9/24 -.023 .001 -29.605 .021
196 2010 ELV0 Seasonal Regressions
Variable B Std. Error t Sig. week 1 (1) - 4/24-4/30 .014 .007 1.992 .117 week 1 (2) - 5/1-5/7 .007 .005 1.623 .180 week 1 (3) - 5/8-5/14 -.002 .009 -.248 .817 week 1 (4) - 5/15-5/21 .001 .003 .427 .691 week 1 (5) - 5/22-5/28 .006 .005 1.274 .272 week 1 (6) - 5/29-6/4 -.006 .006 -.935 .448 week 1 (7) - 6/5-6/11 -.017 .005 -3.659 .067 week 1 (8) - 6/12-6/18 -.007 .006 -1.162 .365 week 1 (9) - 6/19-6/25 -.004 .013 -.299 .793 week 1 (10) - 6/26-7/2 -.005 .009 -.539 .644 week 1 (11) - 7/3-7/9 .012 .011 1.115 .381 week 1 (12) - 7/10-7/16 .024 .034 .699 .557 week 1 (13) - 7/17-7/23 .016 .030 .517 .656 week 1 (14) - 7/17-7/23 -.016 .015 -1.079 .393 week 1 (15) - 7/31-8/6 -.055 .018 -3.116 .198 week 1 (16) - 8/7-8/13 -.044 .011 -3.959 .158 week 1 (17) - 8/14-8/20 -.070 .041 -1.687 .341 week 1 (18) - 8/21-8/27 -.036 .043 -.842 .555 week 1 (19) - 8/28-9/3 -.029 .017 -1.781 .326 week 1 (20) - 9/4-9-10 -.011 .010 -1.140 .458 week 1 (21) - 9/11-9/17 -.014 .011 -1.250 .429 week 1 (22) - 9/18-9/24 -.012 .015 -.791 .574 week 2 (1) - 4/24-5/7 .011 .005 2.143 .099 week 2 (2) - 5/8-5/21 .000 .005 -.094 .930 week 2 (3) - 5/22-6/4 .001 .008 .185 .870 week 2 (4) - 6/5-6/18 -.012 .003 -4.162 .053 week 2 (5) - 6/19-7/2 -.005 .011 -.427 .711 week 2 (6) - 7/3-7/16 .018 .022 .815 .501 week 2 (7) - 7/17-7/30 .000 .021 .015 .989 week 2 (8) - 7/31-8/13 -.049 .003 -17.218 .037 week 2 (9) - 8/14-8/27 -.053 .042 -1.253 .429 week 2 (10) - 8/28-9/10 -.020 .003 -5.854 .108 week 2 (11) - 9/11-9/24 -.012 .013 -.954 .515 week 3 (1) - 4/24-5/14 .014 .007 2.054 .109 week 3 (2) - 5/15-6/4 .004 .003 1.704 .230 week 3 (3) - 6/5-6/25 -.006 .004 -1.364 .306 week 3 (4) - 6/26-7/16 -.003 .010 -.286 .802 week 3 (5) - 7/17-8/6 -.047 .010 -4.459 .140 week 3 (6) - 8/7-8/27 -.046 .014 -3.411 .182 Continued on page 198
197 2010 ELV0 Seasonal Regressions: Continued Variable B Std. Error t Sig. week 4 (1) - 4/24-5/21 .005 .002 2.274 .085 week 4 (2) - 5/22-6/18 -.005 .005 -1.075 .395 week 4 (3) - 6/19-7/16 .007 .016 .434 .707 week 4 (4) - 7/17-8/13 -.038 .032 -1.203 .441 week 4 (5) - 8/14-9/10 -.037 .024 -1.546 .366 week 5 (1) - 4/24-5/28 .005 .006 .828 .495 week 5 (2) - 5/29-7/2 -.008 .006 -1.267 .333 week 5 (3) - 7/3-8/6 -.021 .053 -.396 .760 week 5 (4) - 8/7-9/10 -.038 .020 -1.869 .313 week 6 (1) - 4/24-6/4 .014 .009 1.605 .250 week 6 (2) - 6/5-7/16 -.004 .007 -.599 .610 week 6 (3) - 7/17-8/27 -.045 .009 -4.730 .133 week 7 (1) - 4/24-6/11 .001 .003 .218 .848 week 7 (2) - 6/12-7/30 .003 .016 .188 .868 week 7 (3) - 7/31-9/17 -.037 .013 -2.750 .222 week 8 (1) - 4/24-6/18 -.001 .003 -.219 .847 week 8 (2) - 6/19-8/13 -.045 .010 -4.757 .132 week 9 (1) - 4/24-6/25 -.001 .004 -.280 .806 week 9 (2) - 6/26-8/27 -.031 .021 -1.522 .370 week 10 (1) - 4/24-7/2 -.001 .004 -.321 .779 week 10 (2) - 7/3-9/10 -.030 .016 -1.792 .324 week 11 (1) - 4/24-7/9 .000 .004 -.017 .988 week 11 (2) - 7/10-9/24 -.029 .009 -3.094 .199 week 12 (1) - 4/24-7/16 .005 .005 1.030 .411 week 13 - 4/24-7/23 .003 .009 .339 .767 week 14 - 4/26-7/30 .002 .009 .186 .869 week 15 - 4/24-8/6 -.010 .025 -.411 .752 week 16 - 4/26-8/13 -.022 .009 -2.350 .256 week 17 - 4/26-8/13 -.012 .025 -.479 .716 week 18 - 4/24-8/27 -.015 .018 -.822 .562 week 19 - 4/24-9/3 -.017 .015 -1.121 .464 week 20 - 4/24-9/10 -.017 .014 -1.160 .453 week 21 - 4/26-9/17 -.016 .014 -1.203 .441 week 22 - 4/24-9/24 -.016 .012 -1.374 .401
198 2010 ELV3 Seasonal Regressions
Variable B Std. Error t Sig. week 1 (1) - 4/24-4/30 .014 .007 1.831 .141 week 1 (2) - 5/1-5/7 .007 .005 1.523 .202 week 1 (3) - 5/8-5/14 -.002 .009 -.207 .846 week 1 (4) - 5/15-5/21 .001 .003 .455 .673 week 1 (5) - 5/22-5/28 .006 .005 1.246 .281 week 1 (6) - 5/29-6/4 -.005 .006 -.756 .529 week 1 (7) - 6/5-6/11 -.016 .005 -3.090 .091 week 1 (8) - 6/12-6/18 -.007 .007 -1.067 .398 week 1 (9) - 6/19-6/25 -.003 .013 -.255 .822 week 1 (10) - 6/26-7/2 -.004 .009 -.466 .687 week 1 (11) - 7/3-7/9 .012 .011 1.152 .369 week 1 (12) - 7/10-7/16 .023 .035 .654 .580 week 1 (13) - 7/17-7/23 .015 .031 .493 .671 week 1 (14) - 7/17-7/23 -.015 .015 -.973 .433 week 1 (15) - 7/31-8/6 -.068 .014 -4.722 .133 week 1 (16) - 8/7-8/13 -.051 .019 -2.745 .222 week 1 (17) - 8/14-8/20 -.078 .058 -1.352 .406 week 1 (18) - 8/21-8/27 -.038 .055 -.681 .620 week 1 (19) - 8/28-9/3 -.033 .023 -1.420 .391 week 1 (20) - 9/4-9-10 -.015 .010 -1.406 .394 week 1 (21) - 9/11-9/17 -.015 .015 -1.018 .494 week 1 (22) - 9/18-9/24 -.013 .020 -.638 .639 week 2 (1) - 4/24-5/7 .010 .005 1.965 .121 week 2 (2) - 5/8-5/21 .000 .005 -.046 .966 week 2 (3) - 5/22-6/4 .002 .008 .213 .851 week 2 (4) - 6/5-6/18 -.011 .004 -3.189 .086 week 2 (5) - 6/19-7/2 -.004 .011 -.373 .745 week 2 (6) - 7/3-7/16 .017 .022 .783 .515 week 2 (7) - 7/17-7/30 .000 .021 .023 .984 week 2 (8) - 7/31-8/13 -.059 .003 -22.776 .028 week 2 (9) - 8/14-8/27 -.058 .057 -1.020 .494 week 2 (10) - 8/28-9/10 -.023 .006 -3.598 .173 week 2 (11) - 9/11-9/24 -.013 .017 -.776 .580 week 3 (1) - 4/24-5/14 .013 .007 1.874 .134 week 3 (2) - 5/15-6/4 .004 .003 1.631 .244 week 3 (3) - 6/5-6/25 -.006 .004 -1.288 .327 week 3 (4) - 6/26-7/16 -.002 .010 -.225 .843 week 3 (5) - 7/17-8/6 -.057 .007 -8.384 .076 week 3 (6) - 8/7-8/27 -.057 .011 -5.396 .117 Continued on page 200
199 2010 ELV3 Seasonal Regressions: Continued Variable B Std. Error t Sig. week 4 (1) - 4/24-5/21 .005 .002 2.157 .097 week 4 (2) - 5/22-6/18 -.005 .005 -.978 .431 week 4 (3) - 6/19-7/16 .007 .016 .436 .705 week 4 (4) - 7/17-8/13 -.049 .033 -1.489 .377 week 4 (5) - 8/14-9/10 -.041 .033 -1.247 .430 week 5 (1) - 4/24-5/28 .004 .006 .680 .567 week 5 (2) - 5/29-7/2 -.007 .006 -1.117 .380 week 5 (3) - 7/3-8/6 -.032 .061 -.519 .695 week 5 (4) - 8/7-9/10 -.043 .029 -1.483 .378 week 6 (1) - 4/24-6/4 .013 .010 1.327 .316 week 6 (2) - 6/5-7/16 -.004 .007 -.536 .645 week 6 (3) - 7/17-8/27 -.052 .017 -3.119 .198 week 7 (1) - 4/24-6/11 .000 .003 .113 .920 week 7 (2) - 6/12-7/30 .003 .016 .195 .864 week 7 (3) - 7/31-9/17 -.042 .021 -2.067 .287 week 8 (1) - 4/24-6/18 -.001 .003 -.306 .789 week 8 (2) - 6/19-8/13 -.055 .006 -9.462 .067 week 9 (1) - 4/24-6/25 -.001 .004 -.318 .781 week 9 (2) - 6/26-8/27 -.040 .021 -1.924 .305 week 10 (1) - 4/24-7/2 -.001 .004 -.348 .761 week 10 (2) - 7/3-9/10 -.037 .016 -2.319 .259 week 11 (1) - 4/24-7/9 .000 .004 -.029 .980 week 11 (2) - 7/10-9/24 -.036 .008 -4.676 .134 week 12 (1) - 4/24-7/16 .004 .005 .900 .463 week 13 - 4/24-7/23 .003 .009 .314 .783 week 14 - 4/26-7/30 .002 .009 .175 .877 week 15 - 4/24-8/6 -.016 .029 -.535 .687 week 16 - 4/26-8/13 -.028 .009 -3.228 .191 week 17 - 4/26-8/13 -.017 .028 -.611 .651 week 18 - 4/24-8/27 -.020 .020 -1.010 .497 week 19 - 4/24-9/3 -.022 .016 -1.381 .399 week 20 - 4/24-9/10 -.022 .015 -1.432 .388 week 21 - 4/26-9/17 -.021 .014 -1.489 .377 week 22 - 4/24-9/24 -.021 .012 -1.717 .336
200 2010 CAUV0 Seasonal Regressions
Variable B Std. Error t Sig. week 1 (1) - 4/24-4/30 -.009 .007 -1.355 .247 week 1 (2) - 5/1-5/7 -.007 .003 -2.038 .111 week 1 (3) - 5/8-5/14 .003 .007 .415 .699 week 1 (4) - 5/15-5/21 .000 .002 .073 .945 week 1 (5) - 5/22-5/28 -.004 .004 -.980 .383 week 1 (6) - 5/29-6/4 .004 .001 2.879 .102 week 1 (7) - 6/5-6/11 .006 .003 1.622 .246 week 1 (8) - 6/12-6/18 .002 .003 .597 .611 week 1 (9) - 6/19-6/25 .001 .006 .109 .923 week 1 (10) - 6/26-7/2 .002 .004 .444 .700 week 1 (11) - 7/3-7/9 -.004 .005 -.682 .565 week 1 (12) - 7/10-7/16 -.015 .012 -1.271 .332 week 1 (13) - 7/17-7/23 -.011 .011 -.964 .437 week 1 (14) - 7/17-7/23 .005 .007 .683 .565 week 1 (15) - 7/31-8/6 .007 .009 .714 .605 week 1 (16) - 8/7-8/13 .008 .003 2.414 .250 week 1 (17) - 8/14-8/20 .016 .002 9.480 .067 week 1 (18) - 8/21-8/27 .011 .002 4.251 .147 week 1 (19) - 8/28-9/3 .007 .001 7.736 .082 week 1 (20) - 9/4-9-10 .001 .003 .214 .866 week 1 (21) - 9/11-9/17 .003 .000 29.081 .022 week 1 (22) - 9/18-9/24 .004 .001 3.743 .166 week 2 (1) - 4/24-5/7 -.008 .005 -1.742 .156 week 2 (2) - 5/8-5/21 .002 .004 .458 .671 week 2 (3) - 5/22-6/4 -.001 .003 -.267 .815 week 2 (4) - 6/5-6/18 .004 .003 1.450 .284 week 2 (5) - 6/19-7/2 .001 .005 .243 .830 week 2 (6) - 7/3-7/16 -.009 .008 -1.123 .378 week 2 (7) - 7/17-7/30 -.003 .009 -.358 .755 week 2 (8) - 7/31-8/13 .007 .006 1.192 .445 week 2 (9) - 8/14-8/27 .013 .000 30.106 .021 week 2 (10) - 8/28-9/10 .004 .002 1.964 .300 week 2 (11) - 9/11-9/24 .003 .001 5.865 .108 week 3 (1) - 4/24-5/14 -.011 .006 -1.897 .131 week 3 (2) - 5/15-6/4 -.002 .001 -1.338 .313 week 3 (3) - 6/5-6/25 .001 .002 .489 .673 week 3 (4) - 6/26-7/16 .001 .004 .268 .814 week 3 (5) - 7/17-8/6 .006 .007 .859 .548 week 3 (6) - 8/7-8/27 .006 .008 .753 .589 Continued on page 202
201 2010 CAUV0 Seasonal Regressions: Continued Variable B Std. Error t Sig. week 4 (1) - 4/24-5/21 -.003 .002 -1.446 .222 week 4 (2) - 5/22-6/18 .001 .002 .619 .599 week 4 (3) - 6/19-7/16 -.004 .006 -.644 .586 week 4 (4) - 7/17-8/13 .002 .009 .242 .849 week 4 (5) - 8/14-9/10 .008 .001 15.108 .042 week 5 (1) - 4/24-5/28 -.004 .001 -7.646 .017 week 5 (2) - 5/29-7/2 .003 .003 .959 .439 week 5 (3) - 7/3-8/6 -.003 .011 -.269 .832 week 5 (4) - 8/7-9/10 .008 .001 6.665 .095 week 6 (1) - 4/24-6/4 -.007 .002 -4.146 .054 week 6 (2) - 6/5-7/16 .001 .003 .359 .754 week 6 (3) - 7/17-8/27 .008 .004 2.170 .275 week 7 (1) - 4/24-6/11 -.002 .001 -2.409 .138 week 7 (2) - 6/12-7/30 -.003 .006 -.471 .684 week 7 (3) - 7/31-9/17 .007 .002 3.333 .186 week 8 (1) - 4/24-6/18 -.001 .001 -1.134 .375 week 8 (2) - 6/19-8/13 .006 .007 .883 .539 week 9 (1) - 4/24-6/25 -.001 .001 -.580 .620 week 9 (2) - 6/26-8/27 .003 .007 .364 .778 week 10 (1) - 4/24-7/2 -.001 .002 -.459 .691 week 10 (2) - 7/3-9/10 .003 .006 .448 .732 week 11 (1) - 4/24-7/9 -.001 .002 -.528 .651 week 11 (2) - 7/10-9/24 .003 .005 .711 .607 week 12 (1) - 4/24-7/16 -.003 .001 -3.011 .095 week 13 - 4/24-7/23 -.003 .003 -.882 .471 week 14 - 4/26-7/30 -.002 .004 -.650 .582 week 15 - 4/24-8/6 -.001 .005 -.256 .840 week 16 - 4/26-8/13 .002 .004 .584 .664 week 17 - 4/26-8/13 -.001 .005 -.196 .877 week 18 - 4/24-8/27 .000 .005 .048 .969 week 19 - 4/24-9/3 .001 .004 .205 .871 week 20 - 4/24-9/10 .001 .004 .223 .861 week 21 - 4/26-9/17 .001 .004 .242 .849 week 22 - 4/24-9/24 .001 .004 .311 .808
202 2010 CAUV3 Seasonal Regressions
Variable B Std. Error t Sig. week 1 (1) - 4/24-4/30 -.009 .008 -1.134 .320 week 1 (2) - 5/1-5/7 -.007 .004 -1.878 .134 week 1 (3) - 5/8-5/14 .002 .008 .211 .843 week 1 (4) - 5/15-5/21 .001 .002 .246 .818 week 1 (5) - 5/22-5/28 -.004 .005 -.796 .471 week 1 (6) - 5/29-6/4 .004 .002 2.411 .137 week 1 (7) - 6/5-6/11 .004 .005 .798 .509 week 1 (8) - 6/12-6/18 .001 .003 .406 .724 week 1 (9) - 6/19-6/25 .001 .006 .142 .900 week 1 (10) - 6/26-7/2 .002 .004 .436 .706 week 1 (11) - 7/3-7/9 -.002 .006 -.276 .809 week 1 (12) - 7/10-7/16 -.012 .014 -.888 .468 week 1 (13) - 7/17-7/23 -.008 .012 -.694 .559 week 1 (14) - 7/17-7/23 .004 .007 .513 .659 week 1 (15) - 7/31-8/6 .006 .009 .688 .616 week 1 (16) - 8/7-8/13 .008 .004 2.299 .261 week 1 (17) - 8/14-8/20 .016 .002 8.118 .078 week 1 (18) - 8/21-8/27 .011 .002 4.611 .136 week 1 (19) - 8/28-9/3 .006 .001 6.799 .093 week 1 (20) - 9/4-9-10 .001 .003 .195 .877 week 1 (21) - 9/11-9/17 .003 .000 59.149 .011 week 1 (22) - 9/18-9/24 .004 .001 4.024 .155 week 2 (1) - 4/24-5/7 -.008 .005 -1.504 .207 week 2 (2) - 5/8-5/21 .001 .004 .305 .776 week 2 (3) - 5/22-6/4 .000 .003 -.103 .927 week 2 (4) - 6/5-6/18 .003 .003 .778 .518 week 2 (5) - 6/19-7/2 .001 .005 .249 .827 week 2 (6) - 7/3-7/16 -.007 .009 -.722 .545 week 2 (7) - 7/17-7/30 -.002 .009 -.273 .810 week 2 (8) - 7/31-8/13 .007 .006 1.150 .456 week 2 (9) - 8/14-8/27 .013 .000 63.546 .010 week 2 (10) - 8/28-9/10 .003 .002 1.882 .311 week 2 (11) - 9/11-9/24 .003 .001 6.554 .096 week 3 (1) - 4/24-5/14 -.011 .006 -1.809 .145 week 3 (2) - 5/15-6/4 -.001 .001 -.704 .554 week 3 (3) - 6/5-6/25 .001 .002 .254 .823 week 3 (4) - 6/26-7/16 .001 .004 .332 .771 week 3 (5) - 7/17-8/6 .006 .007 .830 .559 week 3 (6) - 8/7-8/27 .006 .008 .726 .600 Continued on page 204
203 2010 CAUV3 Seasonal Regressions: Continued Variable B Std. Error t Sig. week 4 (1) - 4/24-5/21 -.003 .002 -1.405 .233 week 4 (2) - 5/22-6/18 .001 .002 .451 .696 week 4 (3) - 6/19-7/16 -.003 .007 -.413 .719 week 4 (4) - 7/17-8/13 .002 .009 .223 .860 week 4 (5) - 8/14-9/10 .008 .001 11.939 .053 week 5 (1) - 4/24-5/28 -.004 .001 -3.375 .078 week 5 (2) - 5/29-7/2 .002 .003 .725 .544 week 5 (3) - 7/3-8/6 -.003 .011 -.288 .821 week 5 (4) - 8/7-9/10 .008 .001 5.954 .106 week 6 (1) - 4/24-6/4 -.006 .003 -2.008 .182 week 6 (2) - 6/5-7/16 .001 .003 .317 .781 week 6 (3) - 7/17-8/27 .008 .004 2.074 .286 week 7 (1) - 4/24-6/11 -.002 .001 -2.982 .096 week 7 (2) - 6/12-7/30 -.002 .007 -.327 .774 week 7 (3) - 7/31-9/17 .007 .002 3.134 .197 week 8 (1) - 4/24-6/18 -.001 .001 -1.517 .269 week 8 (2) - 6/19-8/13 .006 .007 .852 .551 week 9 (1) - 4/24-6/25 -.001 .001 -.689 .562 week 9 (2) - 6/26-8/27 .002 .007 .345 .789 week 10 (1) - 4/24-7/2 -.001 .002 -.551 .637 week 10 (2) - 7/3-9/10 .003 .006 .428 .743 week 11 (1) - 4/24-7/9 -.001 .002 -.483 .677 week 11 (2) - 7/10-9/24 .003 .005 .685 .618 week 12 (1) - 4/24-7/16 -.003 .002 -1.756 .221 week 13 - 4/24-7/23 -.002 .003 -.696 .559 week 14 - 4/26-7/30 -.002 .004 -.524 .653 week 15 - 4/24-8/6 -.001 .005 -.275 .829 week 16 - 4/26-8/13 .002 .004 .560 .675 week 17 - 4/26-8/13 -.001 .005 -.215 .865 week 18 - 4/24-8/27 .000 .005 .031 .981 week 19 - 4/24-9/3 .001 .004 .187 .882 week 20 - 4/24-9/10 .001 .004 .204 .872 week 21 - 4/26-9/17 .001 .004 .223 .860 week 22 - 4/24-9/24 .001 .004 .292 .819
204
2010 SOIL0 Seasonal Regressions
Variable B Std. Error t Sig. week 1 (1) - 4/24-4/30 -.005 .004 -1.270 .273 week 1 (2) - 5/1-5/7 -.002 .003 -.535 .621 week 1 (3) - 5/8-5/14 .003 .004 .755 .492 week 1 (4) - 5/15-5/21 .000 .001 -.376 .726 week 1 (5) - 5/22-5/28 -.002 .003 -.967 .388 week 1 (6) - 5/29-6/4 .003 .002 1.188 .357 week 1 (7) - 6/5-6/11 .002 .006 .400 .728 week 1 (8) - 6/12-6/18 .000 .004 -.071 .950 week 1 (9) - 6/19-6/25 -.001 .006 -.208 .855 week 1 (10) - 6/26-7/2 .000 .005 .030 .979 week 1 (11) - 7/3-7/9 -.001 .006 -.235 .836 week 1 (12) - 7/10-7/16 -.014 .015 -.954 .441 week 1 (13) - 7/17-7/23 -.011 .013 -.841 .489 week 1 (14) - 7/17-7/23 .000 .008 .032 .978 week 1 (15) - 7/31-8/6 .004 .012 .330 .797 week 1 (16) - 8/7-8/13 .008 .006 1.202 .442 week 1 (17) - 8/14-8/20 .017 .007 2.323 .259 week 1 (18) - 8/21-8/27 .012 .001 14.203 .045 week 1 (19) - 8/28-9/3 .007 .003 2.181 .274 week 1 (20) - 9/4-9-10 .000 .003 -.091 .942 week 1 (21) - 9/11-9/17 .004 .001 3.657 .170 week 1 (22) - 9/18-9/24 .004 .000 24.826 .026 week 2 (1) - 4/24-5/7 -.004 .003 -1.076 .342 week 2 (2) - 5/8-5/21 .001 .002 .610 .575 week 2 (3) - 5/22-6/4 -.001 .003 -.327 .775 week 2 (4) - 6/5-6/18 .001 .004 .253 .824 week 2 (5) - 6/19-7/2 -.001 .005 -.128 .910 week 2 (6) - 7/3-7/16 -.008 .010 -.747 .533 week 2 (7) - 7/17-7/30 -.005 .009 -.574 .624 week 2 (8) - 7/31-8/13 .006 .009 .643 .636 week 2 (9) - 8/14-8/27 .015 .004 3.640 .171 week 2 (10) - 8/28-9/10 .003 .003 1.026 .492 week 2 (11) - 9/11-9/24 .004 .001 7.506 .084 week 3 (1) - 4/24-5/14 -.005 .004 -1.206 .294 week 3 (2) - 5/15-6/4 -.001 .002 -.509 .661 week 3 (3) - 6/5-6/25 -.001 .003 -.229 .840 week 3 (4) - 6/26-7/16 .000 .005 -.014 .990 Continued on page 206
205 2010 SOIL0 Seasonal Regressions: Continued Variable B Std. Error t Sig. week 3 (6) - 8/7-8/27 .004 .010 .358 .781 week 3 (7) - 8/28-9/17 -.002 .003 -.635 .640 week 4 (1) - 4/24-5/21 -.001 .002 -.676 .536 week 4 (2) - 5/22-6/18 .000 .003 -.024 .983 week 4 (3) - 6/19-7/16 -.004 .007 -.575 .623 week 4 (4) - 7/17-8/13 -.001 .011 -.064 .959 week 4 (5) - 8/14-9/10 .009 .003 2.598 .234 week 5 (1) - 4/24-5/28 -.004 .002 -2.154 .164 week 5 (2) - 5/29-7/2 .001 .004 .198 .862 week 5 (3) - 7/3-8/6 -.007 .011 -.633 .641 week 5 (4) - 8/7-9/10 .009 .004 2.069 .287 week 6 (1) - 4/24-6/4 -.005 .005 -1.035 .409 week 6 (2) - 6/5-7/16 .000 .004 -.093 .934 week 6 (3) - 7/17-8/27 .008 .007 1.110 .467 week 7 (1) - 4/24-6/11 -.002 .000 -4.330 .049 week 7 (2) - 6/12-7/30 -.004 .007 -.573 .625 week 7 (3) - 7/31-9/17 .007 .005 1.485 .377 week 8 (1) - 4/24-6/18 -.002 .001 -2.868 .103 week 8 (2) - 6/19-8/13 .004 .009 .449 .731 week 9 (1) - 4/24-6/25 -.002 .001 -1.244 .339 week 9 (2) - 6/26-8/27 .000 .008 .048 .969 week 10 (1) - 4/24-7/2 -.002 .002 -1.057 .401 week 10 (2) - 7/3-9/10 .001 .008 .121 .923 week 11 (1) - 4/24-7/9 -.001 .002 -.839 .490 week 11 (2) - 7/10-9/24 .002 .006 .328 .798 week 12 (1) - 4/24-7/16 -.003 .002 -1.418 .292 week 13 - 4/24-7/23 -.003 .003 -.928 .451 week 14 - 4/26-7/30 -.003 .004 -.800 .508 week 15 - 4/24-8/6 -.003 .005 -.616 .649 week 16 - 4/26-8/13 .001 .005 .231 .856 week 17 - 4/26-8/13 -.003 .005 -.540 .685 week 18 - 4/24-8/27 -.001 .005 -.259 .839 week 19 - 4/24-9/3 -.001 .005 -.100 .937 week 20 - 4/24-9/10 .000 .005 -.082 .948 week 21 - 4/26-9/17 .000 .005 -.064 .959 week 22 - 4/24-9/24 .000 .005 .000 1.000
206 2010 SOIL3 Seasonal Regressions
Variable B Std. Error t Sig. week 1 (1) - 4/24-4/30 -.009 .005 -1.797 .147 week 1 (2) - 5/1-5/7 -.002 .004 -.643 .555 week 1 (3) - 5/8-5/14 .006 .005 1.314 .259 week 1 (4) - 5/15-5/21 .000 .002 -.222 .835 week 1 (5) - 5/22-5/28 -.003 .003 -.878 .429 week 1 (6) - 5/29-6/4 .005 .002 2.432 .136 week 1 (7) - 6/5-6/11 .006 .006 .991 .426 week 1 (8) - 6/12-6/18 .002 .004 .406 .724 week 1 (9) - 6/19-6/25 .001 .007 .077 .946 week 1 (10) - 6/26-7/2 .002 .005 .381 .740 week 1 (11) - 7/3-7/9 -.003 .007 -.427 .711 week 1 (12) - 7/10-7/16 -.018 .016 -1.075 .395 week 1 (13) - 7/17-7/23 -.013 .015 -.845 .487 week 1 (14) - 7/17-7/23 .005 .009 .505 .664 week 1 (15) - 7/31-8/6 .008 .012 .636 .639 week 1 (16) - 8/7-8/13 .010 .005 2.092 .284 week 1 (17) - 8/14-8/20 .020 .003 6.265 .101 week 1 (18) - 8/21-8/27 .014 .002 5.563 .113 week 1 (19) - 8/28-9/3 .008 .002 5.442 .116 week 1 (20) - 9/4-9-10 .001 .004 .159 .900 week 1 (21) - 9/11-9/17 .004 .000 53.189 .012 week 1 (22) - 9/18-9/24 .005 .001 4.741 .132 week 2 (1) - 4/24-5/7 -.005 .004 -1.424 .227 week 2 (2) - 5/8-5/21 .003 .003 1.189 .300 week 2 (3) - 5/22-6/4 -.001 .004 -.206 .856 week 2 (4) - 6/5-6/18 .004 .004 .905 .461 week 2 (5) - 6/19-7/2 .001 .006 .192 .866 week 2 (6) - 7/3-7/16 -.010 .012 -.897 .464 week 2 (7) - 7/17-7/30 -.004 .011 -.360 .753 week 2 (8) - 7/31-8/13 .009 .008 1.070 .478 week 2 (9) - 8/14-8/27 .017 .000 50.073 .013 week 2 (10) - 8/28-9/10 .004 .003 1.730 .334 week 2 (11) - 9/11-9/24 .004 .001 8.604 .074 week 3 (1) - 4/24-5/14 -.007 .005 -1.479 .213 week 3 (2) - 5/15-6/4 -.002 .002 -.900 .463 week 3 (3) - 6/5-6/25 .001 .003 .270 .813 week 3 (4) - 6/26-7/16 .001 .006 .258 .821 week 3 (5) - 7/17-8/6 .007 .009 .771 .582 Continued on page 208
207 2010 SOIL3 Seasonal Regressions Variable B Std. Error t Sig. week 3 (7) - 8/28-9/17 -.001 .004 -.329 .798 week 4 (1) - 4/24-5/21 -.001 .002 -.610 .575 week 4 (2) - 5/22-6/18 .001 .003 .443 .701 week 4 (3) - 6/19-7/16 -.005 .008 -.540 .643 week 4 (4) - 7/17-8/13 .002 .012 .186 .883 week 4 (5) - 8/14-9/10 .011 .001 8.345 .076 week 5 (1) - 4/24-5/28 -.005 .001 -4.701 .042 week 5 (2) - 5/29-7/2 .003 .004 .731 .541 week 5 (3) - 7/3-8/6 -.004 .014 -.327 .799 week 5 (4) - 8/7-9/10 .010 .002 4.881 .129 week 6 (1) - 4/24-6/4 -.009 .004 -2.326 .146 week 6 (2) - 6/5-7/16 .001 .004 .273 .810 week 6 (3) - 7/17-8/27 .010 .005 1.897 .309 week 7 (1) - 4/24-6/11 -.002 .001 -3.297 .081 week 7 (2) - 6/12-7/30 -.004 .008 -.432 .708 week 7 (3) - 7/31-9/17 .009 .003 2.786 .219 week 8 (1) - 4/24-6/18 -.002 .001 -1.535 .265 week 8 (2) - 6/19-8/13 .007 .009 .793 .573 week 9 (1) - 4/24-6/25 -.001 .002 -.738 .538 week 9 (2) - 6/26-8/27 .003 .009 .305 .811 week 10 (1) - 4/24-7/2 -.001 .002 -.597 .611 week 10 (2) - 7/3-9/10 .003 .008 .386 .765 week 11 (1) - 4/24-7/9 -.001 .002 -.568 .628 week 11 (2) - 7/10-9/24 .004 .006 .634 .640 week 12 (1) - 4/24-7/16 -.004 .002 -2.179 .161 week 13 - 4/24-7/23 -.003 .004 -.829 .494 week 14 - 4/26-7/30 -.003 .005 -.632 .592 week 15 - 4/24-8/6 -.002 .007 -.314 .807 week 16 - 4/26-8/13 .003 .005 .514 .698 week 17 - 4/26-8/13 -.002 .007 -.252 .843 week 18 - 4/24-8/27 .000 .006 -.005 .997 week 19 - 4/24-9/3 .001 .006 .150 .905 week 20 - 4/24-9/10 .001 .005 .167 .894 week 21 - 4/26-9/17 .001 .005 .186 .883 week 22 - 4/24-9/24 .001 .005 .253 .842
208 2010 MKT0 Seasonal Regressions
Variable B Std. Error t Sig. week 1 (1) - 4/24-4/30 -.011 .007 -1.621 .180 week 1 (2) - 5/1-5/7 -.001 .005 -.123 .908 week 1 (3) - 5/8-5/14 .012 .005 2.109 .103 week 1 (4) - 5/15-5/21 -.001 .002 -.580 .593 week 1 (5) - 5/22-5/28 -.004 .004 -.885 .426 week 1 (6) - 5/29-6/4 .007 .006 1.216 .348 week 1 (7) - 6/5-6/11 .018 .005 3.926 .059 week 1 (8) - 6/12-6/18 .003 .009 .337 .768 week 1 (9) - 6/19-6/25 -.005 .015 -.314 .783 week 1 (10) - 6/26-7/2 .000 .011 .010 .993 week 1 (11) - 7/3-7/9 -.017 .009 -1.948 .191 week 1 (12) - 7/10-7/16 -.052 .020 -2.594 .122 week 1 (13) - 7/17-7/23 -.040 .021 -1.891 .199 week 1 (14) - 7/17-7/23 .007 .020 .358 .754 week 1 (15) - 7/31-8/6 .012 .035 .341 .791 week 1 (16) - 8/7-8/13 .022 .018 1.226 .436 week 1 (17) - 8/14-8/20 .047 .020 2.388 .252 week 1 (18) - 8/21-8/27 .035 .002 16.536 .038 week 1 (19) - 8/28-9/3 .020 .009 2.239 .267 week 1 (20) - 9/4-9-10 -.001 .009 -.081 .948 week 1 (21) - 9/11-9/17 .011 .003 3.804 .164 week 1 (22) - 9/18-9/24 .012 .000 32.920 .019 week 2 (1) - 4/24-5/7 -.006 .006 -1.034 .359 week 2 (2) - 5/8-5/21 .005 .003 1.638 .177 week 2 (3) - 5/22-6/4 -.006 .007 -.871 .476 week 2 (4) - 6/5-6/18 .011 .006 1.700 .231 week 2 (5) - 6/19-7/2 -.002 .013 -.165 .884 week 2 (6) - 7/3-7/16 -.034 .013 -2.591 .122 week 2 (7) - 7/17-7/30 -.017 .020 -.833 .493 week 2 (8) - 7/31-8/13 .017 .026 .657 .630 week 2 (9) - 8/14-8/27 .042 .011 3.786 .164 week 2 (10) - 8/28-9/10 .009 .009 1.047 .485 week 2 (11) - 9/11-9/24 .011 .001 8.118 .078 week 3 (1) - 4/24-5/14 -.007 .007 -.942 .400 week 3 (2) - 5/15-6/4 -.006 .001 -4.529 .045 week 3 (3) - 6/5-6/25 .002 .006 .341 .766 week 3 (4) - 6/26-7/16 -.002 .011 -.212 .852 Continued on page 210
209 2010 MKT0 Seasonal Regressions: Continued Variable B Std. Error t Sig. week 3 (6) - 8/7-8/27 .011 .029 .370 .775 week 3 (7) - 8/28-9/17 -.006 .010 -.621 .646 week 4 (1) - 4/24-5/21 .000 .003 -.143 .893 week 4 (2) - 5/22-6/18 .002 .006 .320 .779 week 4 (3) - 6/19-7/16 -.019 .013 -1.435 .288 week 4 (4) - 7/17-8/13 -.002 .031 -.054 .966 week 4 (5) - 8/14-9/10 .026 .010 2.677 .228 week 5 (1) - 4/24-5/28 -.010 .003 -3.077 .091 week 5 (2) - 5/29-7/2 .005 .008 .582 .619 week 5 (3) - 7/3-8/6 -.019 .031 -.619 .647 week 5 (4) - 8/7-9/10 .025 .012 2.122 .280 week 6 (1) - 4/24-6/4 -.018 .007 -2.391 .139 week 6 (2) - 6/5-7/16 .000 .009 -.018 .987 week 6 (3) - 7/17-8/27 .022 .019 1.133 .460 week 7 (1) - 4/24-6/11 -.004 .002 -1.579 .255 week 7 (2) - 6/12-7/30 -.015 .014 -1.056 .402 week 7 (3) - 7/31-9/17 .021 .014 1.517 .371 week 8 (1) - 4/24-6/18 -.002 .003 -.886 .469 week 8 (2) - 6/19-8/13 .012 .027 .461 .725 week 9 (1) - 4/24-6/25 -.003 .004 -.721 .546 week 9 (2) - 6/26-8/27 .001 .024 .058 .963 week 10 (1) - 4/24-7/2 -.003 .004 -.637 .590 week 10 (2) - 7/3-9/10 .003 .021 .131 .917 week 11 (1) - 4/24-7/9 -.004 .004 -.926 .452 week 11 (2) - 7/10-9/24 .006 .018 .339 .792 week 12 (1) - 4/24-7/16 -.009 .001 -6.036 .026 week 13 - 4/24-7/23 -.010 .007 -1.564 .258 week 14 - 4/26-7/30 -.009 .008 -1.202 .352 week 15 - 4/24-8/6 -.009 .015 -.602 .655 week 16 - 4/26-8/13 .004 .015 .241 .849 week 17 - 4/26-8/13 -.008 .015 -.527 .691 week 18 - 4/24-8/27 -.004 .014 -.248 .845 week 19 - 4/24-9/3 -.001 .014 -.090 .943 week 20 - 4/24-9/10 -.001 .014 -.073 .954 week 21 - 4/26-9/17 -.001 .013 -.054 .966 week 22 - 4/24-9/24 .000 .013 .010 .994
210 2010 MKT3 Seasonal Regressions
Variable B Std. Error T Sig. week 1 (1) - 4/24-4/30 -.011 .007 -1.521 .203 week 1 (2) - 5/1-5/7 .000 .005 -.039 .971 week 1 (3) - 5/8-5/14 .012 .006 2.153 .098 week 1 (4) - 5/15-5/21 -.001 .002 -.265 .804 week 1 (5) - 5/22-5/28 -.002 .005 -.469 .664 week 1 (6) - 5/29-6/4 .011 .007 1.582 .254 week 1 (7) - 6/5-6/11 .026 .006 4.645 .043 week 1 (8) - 6/12-6/18 .012 .009 1.333 .314 week 1 (9) - 6/19-6/25 .008 .020 .390 .734 week 1 (10) - 6/26-7/2 .010 .014 .709 .552 week 1 (11) - 7/3-7/9 -.017 .018 -.969 .435 week 1 (12) - 7/10-7/16 -.041 .051 -.811 .503 week 1 (13) - 7/17-7/23 -.027 .046 -.580 .621 week 1 (14) - 7/17-7/23 .027 .021 1.305 .322 week 1 (15) - 7/31-8/6 .054 .029 1.870 .313 week 1 (16) - 8/7-8/13 .048 .003 14.931 .043 week 1 (17) - 8/14-8/20 .081 .030 2.701 .226 week 1 (18) - 8/21-8/27 .045 .038 1.210 .440 week 1 (19) - 8/28-9/3 .034 .012 2.909 .211 week 1 (20) - 9/4-9-10 .010 .012 .792 .573 week 1 (21) - 9/11-9/17 .017 .009 1.857 .315 week 1 (22) - 9/18-9/24 .015 .014 1.138 .459 week 2 (1) - 4/24-5/7 -.006 .006 -.939 .401 week 2 (2) - 5/8-5/21 .006 .003 1.872 .135 week 2 (3) - 5/22-6/4 -.001 .012 -.116 .918 week 2 (4) - 6/5-6/18 .019 .002 10.054 .010 week 2 (5) - 6/19-7/2 .009 .016 .542 .642 week 2 (6) - 7/3-7/16 -.029 .033 -.876 .473 week 2 (7) - 7/17-7/30 .000 .033 -.009 .993 week 2 (8) - 7/31-8/13 .051 .012 4.112 .152 week 2 (9) - 8/14-8/27 .063 .034 1.862 .314 week 2 (10) - 8/28-9/10 .021 .000 87.883 .007 week 2 (11) - 9/11-9/24 .015 .011 1.376 .400 week 3 (1) - 4/24-5/14 -.006 .008 -.813 .462 week 3 (2) - 5/15-6/4 -.007 .004 -1.757 .221 week 3 (3) - 6/5-6/25 .009 .006 1.428 .289 week 3 (4) - 6/26-7/16 .007 .015 .431 .709 week 3 (5) - 7/17-8/6 .047 .020 2.358 .255 Continued on page 212
211 2010 MKT3 Seasonal Regressions: Continued Variable B Std. Error t Sig. week 3 (7) - 8/28-9/17 .004 .019 .198 .876 week 4 (1) - 4/24-5/21 .000 .003 .003 .998 week 4 (2) - 5/22-6/18 .009 .007 1.267 .333 week 4 (3) - 6/19-7/16 -.010 .025 -.425 .712 week 4 (4) - 7/17-8/13 .034 .040 .837 .556 week 4 (5) - 8/14-9/10 .043 .018 2.408 .251 week 5 (1) - 4/24-5/28 -.010 .008 -1.333 .314 week 5 (2) - 5/29-7/2 .014 .008 1.646 .242 week 5 (3) - 7/3-8/6 .012 .060 .199 .875 week 5 (4) - 8/7-9/10 .043 .014 3.114 .198 week 6 (1) - 4/24-6/4 -.026 .009 -2.884 .102 week 6 (2) - 6/5-7/16 .008 .011 .730 .541 week 6 (3) - 7/17-8/27 .048 .001 35.950 .018 week 7 (1) - 4/24-6/11 -.003 .005 -.536 .645 week 7 (2) - 6/12-7/30 -.004 .025 -.180 .874 week 7 (3) - 7/31-9/17 .041 .007 5.888 .107 week 8 (1) - 4/24-6/18 .000 .004 -.035 .975 week 8 (2) - 6/19-8/13 .045 .019 2.448 .247 week 9 (1) - 4/24-6/25 .001 .006 .148 .896 week 9 (2) - 6/26-8/27 .029 .027 1.048 .485 week 10 (1) - 4/24-7/2 .001 .006 .214 .850 week 10 (2) - 7/3-9/10 .028 .023 1.213 .439 week 11 (1) - 4/24-7/9 .000 .007 -.034 .976 week 11 (2) - 7/10-9/24 .028 .015 1.861 .314 week 12 (1) - 4/24-7/16 -.009 .006 -1.430 .289 week 13 - 4/24-7/23 -.005 .013 -.411 .721 week 14 - 4/26-7/30 -.003 .014 -.228 .841 week 15 - 4/24-8/6 .006 .029 .212 .867 week 16 - 4/26-8/13 .021 .014 1.517 .371 week 17 - 4/26-8/13 .008 .028 .272 .831 week 18 - 4/24-8/27 .012 .022 .556 .677 week 19 - 4/24-9/3 .015 .019 .779 .579 week 20 - 4/24-9/10 .015 .018 .807 .568 week 21 - 4/26-9/17 .014 .017 .837 .556 week 22 - 4/24-9/24 .015 .016 .952 .516
212 Hourly Regression Tables 2009 & 2010 Combined
Std. Variable B Error Beta T Sig. AHI .015 .015 .453 1.017 .367 BD -.014 .008 -.642 -1.674 .169 SOIL -.005 .011 -.240 -.495 .647 SOC -.001 .004 -.145 -.293 .784 ELV .003 .008 .220 .451 .675 MKT -.007 .006 -.513 -1.196 .298 CAUV -.001 .006 -.081 -.163 .879 a. Dependent Variable: 0000
Std. Variable B Error Beta T Sig. AHI .014 .013 .475 1.078 .342 BD -.013 .007 -.674 -1.823 .142 SOIL -.005 .010 -.231 -.476 .659 SOC .000 .004 -.059 -.118 .912 ELV .002 .007 .174 .353 .742 MKT -.005 .005 -.424 -.937 .402 CAUV -.001 .006 -.081 -.163 .878 a. Dependent Variable: 0100
Std. Variable B Error Beta T Sig. AHI .015 .011 .540 1.282 .269 BD -.013 .006 -.711 -2.020 .114 SOIL -.003 .009 -.141 -.284 .790 SOC -.001 .003 -.182 -.371 .730 ELV .003 .006 .249 .514 .634 MKT -.006 .005 -.509 -1.184 .302 CAUV -.002 .005 -.184 -.375 .727 a. Dependent Variable: 0200 (Continued)
213 Hourly Regression Tables 2009 & 2010 Combined: Continued
Std. Variable B Error Beta t Sig. AHI .017 .012 .582 1.430 .226 BD -.014 .007 -.730 -2.138 .099 SOIL -.002 .010 -.086 -.173 .871 SOC -.002 .003 -.238 -.490 .650 ELV .003 .007 .242 .498 .644 MKT -.006 .005 -.533 -1.260 .276 CAUV -.002 .006 -.198 -.404 .707 a. Dependent Variable: 0300
Variable B Std. Beta t Sig. Error AHI .017 .012 .574 1.401 .234 BD -.014 .007 -.732 -2.150 .098 SOIL -.002 .010 -.077 -.155 .884 SOC -.002 .003 -.284 -.592 .586 ELV .004 .007 .281 .587 .589 MKT -.007 .005 -.574 -1.402 .233 CAUV -.003 .006 -.253 -.522 .629 a. Dependent Variable: 0400
Variable B Std. Beta t Sig. Error AHI .022 .011 .693 1.923 .127 BD -.016 .007 -.775 -2.456 .070 SOIL .001 .010 .059 .119 .911 SOC -.001 .004 -.092 -.185 .862 ELV .005 .007 .334 .710 .517 MKT -.004 .006 -.353 -.754 .493 CAUV -.004 .006 -.345 -.735 .503 a. Dependent Variable: 0500 (Continued)
214 Hourly Regression Tables 2009 & 2010 Combined: Continued
Variable B Std. Beta t Sig. Error AHI .019 .011 .644 1.682 .168 BD -.013 .007 -.671 -1.808 .145 SOIL -.001 .010 -.029 -.058 .956 SOC .000 .003 .022 .043 .967 ELV .004 .007 .283 .590 .587 MKT -.003 .006 -.290 -.606 .577 CAUV -.001 .006 -.117 -.237 .825 a. Dependent Variable: 0600
Variable B Std. Beta t Sig. Error AHI .014 .009 .605 1.518 .204 BD -.009 .006 -.582 -1.432 .225 SOIL -.001 .008 -.067 -.133 .900 SOC .000 .003 .051 .101 .924 ELV .003 .005 .309 .651 .551 MKT -.003 .005 -.287 -.598 .582 CAUV .000 .005 -.015 -.030 .978 a. Dependent Variable: 0700
Std. Variable B Error Beta t Sig. AHI .013 .009 .578 1.416 .230 BD -.008 .006 -.555 -1.333 .253 SOIL -.001 .007 -.069 -.138 .897 SOC .001 .003 .101 .202 .849 ELV .004 .005 .396 .862 .437 MKT -.002 .004 -.253 -.523 .629 CAUV -.001 .004 -.089 -.179 .867 a. Dependent Variable: 0800 (Continued)
215 Hourly Regression Tables 2009 & 2010 Combined: Continued
Std. Variable B Error Beta t Sig. AHI .010 .009 .458 1.030 .361 BD -.008 .005 -.602 -1.509 .206 SOIL -.003 .007 -.193 -.394 .714 SOC .001 .002 .107 .216 .840 ELV .003 .005 .344 .733 .504 MKT -.002 .004 -.279 -.581 .592 CAUV -.002 .004 -.195 -.398 .711 a. Dependent Variable: 0900
Std. Variable B Error Beta t Sig. AHI .004 .012 .140 .282 .792 BD -.005 .008 -.318 -.671 .539 SOIL -.008 .007 -.494 -1.136 .319 SOC .001 .003 .138 .279 .794 ELV .002 .006 .210 .429 .690 MKT -.003 .005 -.335 -.712 .516 CAUV .002 .005 .186 .378 .725 a. Dependent Variable: 1000
Std. Variable B Error Beta t Sig. AHI .002 .014 .088 .178 .868 BD -.003 .009 -.139 -.281 .793 SOIL -.009 .008 -.479 -1.090 .337 SOC .001 .003 .159 .323 .763 ELV .004 .006 .279 .580 .593 MKT -.004 .005 -.309 -.649 .552 CAUV .003 .005 .319 .673 .538 a. Dependent Variable: 1100
(Continued)
216 Hourly Regression Tables 2009 & 2010 Combined: Continued Std. Variable B Error Beta t Sig. AHI .000 .017 .002 .004 .997 BD .001 .011 .030 .059 .955 SOIL -.011 .010 -.501 -1.157 .312 SOC .002 .004 .247 .510 .637 ELV .003 .008 .196 .399 .710 MKT -.003 .007 -.201 -.410 .703 CAUV .007 .006 .527 1.242 .282 a. Dependent Variable: 1200
Std. Variable B Error Beta t Sig. AHI -.001 .014 -.018 -.036 .973 BD -.004 .009 -.214 -.439 .684 SOIL -.012 .007 -.610 -1.539 .199 SOC .002 .003 .275 .571 .598 ELV .001 .007 .088 .176 .869 MKT -.002 .006 -.213 -.436 .685 CAUV .003 .005 .296 .620 .569 a. Dependent Variable: 1300
Std. Variable B Error Beta t Sig. AHI -.001 .015 -.029 -.059 .956 BD -.001 .010 -.033 -.065 .951 SOIL -.011 .008 -.546 -1.304 .262 SOC .002 .003 .339 .721 .511 ELV .002 .007 .160 .324 .762 MKT -.001 .006 -.127 -.256 .811 CAUV .005 .005 .437 .973 .386 a. Dependent Variable: 1400 (Continued)
217 Hourly Regression Tables 2009 & 2010 Combined: Continued
Std. Variable B Error Beta t Sig. AHI .001 .012 .021 .042 .968 BD .000 .008 .026 .051 .962 SOIL -.007 .007 -.476 -1.084 .339 SOC .001 .003 .245 .506 .639 ELV .003 .005 .258 .534 .622 MKT -.002 .005 -.205 -.419 .696 CAUV .004 .004 .476 1.083 .340 a. Dependent Variable: 1500
Std. Variable B Error Beta t Sig. AHI .002 .012 .100 .201 .850 BD .000 .008 -.020 -.040 .970 SOIL -.007 .007 -.423 -.935 .403 SOC .002 .003 .319 .673 .538 ELV .002 .005 .153 .309 .773 MKT -.001 .005 -.103 -.207 .846 CAUV .005 .004 .551 1.321 .257 a. Dependent Variable: 1600
Std. Variable B Error Beta t Sig. AHI .003 .010 .133 .268 .802 BD -.004 .006 -.279 -.582 .592 SOIL -.006 .006 -.481 -1.097 .334 SOC .001 .002 .295 .618 .570 ELV .002 .004 .184 .375 .726 MKT -.001 .004 -.175 -.356 .740 CAUV .002 .004 .222 .456 .672 a. Dependent Variable: 1700 (Continued)
218 Hourly Regression Tables 2009 & 2010 Combined: Continued
Std. Variable B Error Beta t Sig. AHI .005 .010 .236 .485 .653 BD -.003 .007 -.197 -.401 .709 SOIL -.005 .006 -.356 -.762 .488 SOC .001 .002 .271 .562 .604 ELV .003 .005 .263 .546 .614 MKT -.001 .004 -.167 -.338 .752 CAUV .003 .004 .337 .716 .514 a. Dependent Variable: 1800
Std. Variable B Error Beta t Sig. AHI .001 .010 .042 .084 .937 BD -.003 .006 -.258 -.533 .622 SOIL -.007 .005 -.570 -1.389 .237 SOC .001 .002 .229 .471 .662 ELV .001 .005 .126 .254 .812 MKT -.002 .004 -.255 -.528 .626 CAUV .002 .004 .259 .536 .621 a. Dependent Variable: 1900
Std. Variable B Error Beta t Sig. AHI .006 .011 .264 .548 .613 BD -.006 .006 -.435 -.967 .388 SOIL -.006 .007 -.414 -.910 .414 SOC .001 .003 .226 .464 .667 ELV .000 .005 -.005 -.010 .992 MKT -.002 .004 -.190 -.387 .719 CAUV .002 .004 .273 .568 .600 a. Dependent Variable: 2000 (Continued)
219 Hourly Regression Tables 2009 & 2010 Combined: Continued
Std. Variable B Error Beta t Sig. AHI .009 .014 .314 .661 .545 BD -.011 .008 -.560 -1.353 .248 SOIL -.008 .009 -.396 -.862 .437 SOC .001 .003 .115 .231 .829 ELV .000 .007 .023 .045 .966 MKT -.004 .006 -.297 -.622 .567 CAUV .001 .006 .121 .243 .820 a. Dependent Variable: 2100
Std. Variable B Error Beta t Sig. AHI .011 .015 .347 .739 .501 BD -.014 .007 -.704 -1.981 .119 SOIL -.007 .010 -.362 -.777 .481 SOC .000 .004 -.066 -.132 .901 ELV .001 .007 .057 .115 .914 MKT -.006 .006 -.451 -1.010 .370 CAUV -.001 .006 -.111 -.224 .834 a. Dependent Variable: 2200
Std. Variable B Error Beta t Sig. AHI .016 .015 .471 1.067 .346 BD -.015 .008 -.702 -1.970 .120 SOIL -.005 .011 -.222 -.456 .672 SOC -.001 .004 -.181 -.368 .731 ELV .003 .008 .162 .328 .760 MKT -.007 .006 -.523 -1.228 .287 CAUV -.002 .006 -.124 -.250 .815 a. Dependent Variable: 2300
220 Hourly regression 2009 & 2010 combined R3
Std. Variable B Error Beta t Sig. AHI .015 .017 .393 .855 .441 BD -.012 .010 -.499 -1.153 .313 SOIL -.011 .020 -.265 -.550 .612 SOC -.004 .005 -.355 -.760 .490 ELV .003 .008 .206 .422 .695 MKT -.008 .006 -.610 -1.541 .198 CAUV .001 .007 .040 .081 .940 a. Dependent Variable: 0000
Std. Variable B Error Beta t Sig. AHI .014 .015 .411 .902 .418 BD -.012 .009 -.559 -1.348 .249 SOIL -.009 .018 -.237 -.487 .651 SOC -.002 .004 -.275 -.573 .598 ELV .002 .007 .163 .330 .758 MKT -.007 .005 -.532 -1.257 .277 CAUV .001 .006 .051 .102 .923 a. Dependent Variable: 0100
Std. Variable B Error Beta t Sig. AHI .014 .013 .465 1.052 .352 BD -.011 .008 -.580 -1.425 .227 SOIL -.006 .017 -.183 -.373 .728 SOC -.003 .004 -.398 -.867 .435 ELV .003 .006 .231 .475 .660 MKT -.007 .005 -.600 -1.499 .208 CAUV -.001 .006 -.074 -.148 .889 a. Dependent Variable: 0200 (Continued)
221 Hourly regression 2009 & 2010 combined R3: Continued
Std. Variable B Error Beta t Sig. AHI .016 .014 .499 1.152 .314 BD -.013 .008 -.599 -1.497 .209 SOIL -.005 .019 -.127 -.255 .811 SOC -.004 .004 -.448 -1.002 .373 ELV .003 .007 .220 .451 .676 MKT -.008 .005 -.625 -1.603 .184 CAUV -.001 .006 -.119 -.240 .822 a. Dependent Variable: 0300
Std. Variable B Error Beta t Sig. AHI .016 .014 .489 1.121 .325 BD -.013 .009 -.588 -1.453 .220 SOIL -.006 .019 -.144 -.291 .786 SOC -.004 .004 -.495 -1.138 .319 ELV .004 .007 .258 .533 .622 MKT -.008 .005 -.651 -1.714 .162 CAUV -.002 .006 -.170 -.345 .747 a. Dependent Variable: 0400
Std. Variable B Error Beta t Sig. AHI .021 .014 .617 1.568 .192 BD -.016 .008 -.719 -2.072 .107 SOIL .000 .020 -.001 -.003 .998 SOC -.003 .004 -.310 -.653 .550 ELV .005 .007 .317 .668 .541 MKT -.006 .006 -.435 -.967 .388 CAUV -.003 .006 -.205 -.420 .696 a. Dependent Variable: 0500 (Continued)
222 Hourly regression 2009 & 2010 combined R3: Continued
Std. Variable B Error Beta t Sig. AHI .019 .013 .596 1.485 .212 BD -.013 .008 -.632 -1.631 .178 SOIL -.001 .018 -.025 -.051 .962 SOC -.002 .004 -.184 -.375 .727 ELV .004 .007 .275 .571 .598 MKT -.005 .006 -.412 -.904 .417 CAUV .000 .006 .030 .060 .955 a. Dependent Variable: 0600
Std. Variable B Error Beta t Sig. AHI .015 .011 .579 1.421 .228 BD -.009 .007 -.545 -1.301 .263 SOIL -.001 .015 -.048 -.096 .928 SOC -.001 .003 -.143 -.289 .787 ELV .003 .005 .306 .642 .556 MKT -.004 .004 -.418 -.920 .410 CAUV .001 .005 .143 .289 .787 a. Dependent Variable: 0700
Std. Variable B Error Beta T Sig. AHI .014 .010 .565 1.369 .243 BD -.008 .007 -.527 -1.241 .283 SOIL -.002 .014 -.085 -.171 .873 SOC -.001 .003 -.097 -.194 .856 ELV .004 .005 .396 .864 .436 MKT -.003 .004 -.361 -.775 .482 CAUV .001 .004 .122 .247 .817 a. Dependent Variable: 0800 (Continued)
223 Hourly regression 2009 & 2010 combined R3: Continued
Std. Variable B Error Beta t Sig. AHI .010 .010 .427 .946 .398 BD -.008 .006 -.539 -1.281 .269 SOIL -.007 .013 -.247 -.511 .636 SOC -.001 .003 -.110 -.222 .835 ELV .003 .005 .344 .733 .504 MKT -.003 .004 -.355 -.759 .490 CAUV .000 .004 .048 .097 .927 a. Dependent Variable: 0900
Std. Variable B Error Beta t Sig. AHI .004 .014 .154 .311 .771 BD -.004 .009 -.201 -.409 .703 SOIL -.015 .014 -.484 -1.105 .331 SOC .000 .004 -.037 -.073 .945 ELV .003 .006 .221 .454 .674 MKT -.004 .005 -.430 -.952 .395 CAUV .004 .005 .410 .899 .419 a. Dependent Variable: 1000
Std. Variable B Error Beta t Sig. AHI .004 .015 .141 .285 .790 BD -.001 .010 -.038 -.076 .943 SOIL -.016 .016 -.454 -1.019 .366 SOC .000 .004 .022 .044 .967 ELV .004 .006 .297 .621 .568 MKT -.005 .005 -.407 -.891 .423 CAUV .006 .005 .545 1.300 .263 a. Dependent Variable: 1100
(Continued)
224 Hourly regression 2009 & 2010 combined R3: Continued
Variable Std. B Error Beta t Sig. AHI .003 .019 .083 .167 .875 BD .002 .012 .099 .199 .852 SOIL -.018 .020 -.414 -.910 .414 SOC .002 .005 .151 .306 .775 ELV .004 .008 .221 .454 .673 MKT -.005 .007 -.322 -.680 .534 CAUV .010 .005 .726 2.112 .102 a. Dependent Variable: 1200
Variable Std. B Error Beta t Sig. AHI .000 .016 .010 .020 .985 BD -.002 .010 -.113 -.227 .831 SOIL -.021 .015 -.564 -1.367 .243 SOC .001 .004 .117 .236 .825 ELV .001 .007 .108 .216 .839 MKT -.004 .006 -.309 -.651 .551 CAUV .006 .005 .535 1.265 .275 a. Dependent Variable: 1300
Variable Std. B Error Beta t Sig. AHI .001 .016 .041 .083 .938 BD .001 .010 .026 .052 .961 SOIL -.018 .016 -.473 -1.075 .343 SOC .002 .004 .223 .458 .671 ELV .003 .007 .188 .382 .722 MKT -.003 .006 -.235 -.484 .654 CAUV .008 .004 .679 1.850 .138 a. Dependent Variable: 1400 (Continued)
225 Hourly regression 2009 & 2010 combined R3: Continued
Variable Std. B Error Beta t Sig. AHI .003 .013 .106 .213 .841 BD .002 .008 .092 .184 .863 SOIL -.012 .014 -.411 -.902 .418 SOC .001 .003 .146 .295 .783 ELV .003 .005 .284 .592 .586 MKT -.003 .005 -.317 -.667 .541 CAUV .007 .003 .695 1.935 .125 a. Dependent Variable: 1500
Variable Std. B Error Beta t Sig. AHI .005 .013 .177 .359 .738 BD .000 .008 .006 .012 .991 SOIL -.009 .014 -.299 -.627 .565 SOC .002 .003 .221 .454 .673 ELV .002 .005 .179 .363 .735 MKT -.002 .005 -.251 -.519 .631 CAUV .007 .003 .738 2.186 .094 a. Dependent Variable: 1600
Variable Std. B Error Beta t Sig. AHI .003 .011 .161 .327 .760 BD -.003 .007 -.213 -.437 .685 SOIL -.011 .011 -.444 -.991 .378 SOC .001 .003 .127 .256 .811 ELV .002 .004 .203 .416 .699 MKT -.002 .004 -.280 -.584 .591 CAUV .004 .003 .479 1.091 .337 a. Dependent Variable: 1700 (Continued)
226 Hourly regression 2009 & 2010 combined R3: Continued
Variable Std. B Error Beta t Sig. AHI .007 .011 .288 .602 .579 BD -.002 .007 -.161 -.327 .760 SOIL -.008 .013 -.289 -.603 .579 SOC .001 .003 .130 .261 .807 ELV .003 .005 .283 .590 .587 MKT -.003 .004 -.299 -.626 .565 CAUV .005 .004 .562 1.360 .246 a. Dependent Variable: 1800
Variable Std. B Error Beta t Sig. AHI .001 .011 .064 .128 .904 BD -.002 .007 -.151 -.305 .775 SOIL -.013 .011 -.535 -1.267 .274 SOC .000 .003 .064 .129 .904 ELV .001 .005 .143 .289 .787 MKT -.003 .004 -.353 -.754 .493 CAUV .004 .004 .492 1.131 .321 a. Dependent Variable: 1900
Variable Std. B Error Beta t Sig. AHI .006 .012 .250 .517 .633 BD -.006 .007 -.372 -.801 .468 SOIL -.009 .013 -.311 -.655 .548 SOC .000 .003 .049 .097 .927 ELV .000 .005 .003 .006 .995 MKT -.003 .004 -.341 -.725 .509 CAUV .004 .004 .428 .948 .397 a. Dependent Variable: 2000 (Continued)
227 Hourly regression 2009 & 2010 combined R3: Continued
Variable Std. B Error Beta t Sig. AHI .009 .016 .271 .563 .604 BD -.010 .009 -.462 -1.041 .357 SOIL -.013 .018 -.337 -.716 .513 SOC -.001 .004 -.088 -.176 .869 ELV .000 .007 .022 .045 .966 MKT -.005 .006 -.428 -.948 .397 CAUV .003 .006 .269 .560 .606 a. Dependent Variable: 2100
Variable Std. B Error Beta t Sig. AHI .009 .017 .264 .547 .613 BD -.012 .009 -.552 -1.325 .256 SOIL -.015 .018 -.376 -.812 .462 SOC -.003 .004 -.292 -.612 .574 ELV .001 .007 .045 .090 .933 MKT -.007 .005 -.541 -1.286 .268 CAUV .000 .006 .019 .038 .972 a. Dependent Variable: 2200
Variable Std. B Error Beta t Sig. AHI .014 .017 .391 .850 .443 BD -.013 .010 -.553 -1.329 .255 SOIL -.010 .020 -.246 -.508 .638 SOC -.004 .005 -.396 -.862 .437 ELV .002 .008 .144 .290 .786 MKT -.009 .005 -.619 -1.577 .190 CAUV .000 .007 -.030 -.060 .955 a. Dependent Variable: 2300
228 Hourly regression 2009 R0
Std. Variable B Error Beta t Sig. AHI -.007 .022 -.167 -.339 .751 BD .013 .013 .454 1.020 .365 SOIL .014 .013 .491 1.126 .323 SOC -.003 .005 -.252 -.521 .630 ELV .004 .010 .215 .439 .683 MKT .002 .009 .137 .276 .796 CAUV -.005 .008 -.319 -.673 .538 a. Dependent Variable: low0000
Std. Variable B Error Beta t Sig. AHI -.010 .018 -.261 -.541 .617 BD .015 .010 .585 1.444 .222 SOIL .011 .011 .424 .937 .402 SOC -.002 .004 -.204 -.417 .698 ELV .004 .009 .202 .412 .702 MKT .003 .008 .164 .333 .756 CAUV -.003 .007 -.180 -.367 .732 a. Dependent Variable: 0100
Std. Variable B Error Beta t Sig. AHI -.011 .017 -.312 -.658 .547 BD .014 .009 .600 1.499 .208 SOIL .008 .011 .358 .767 .486 SOC -.003 .004 -.350 -.746 .497 ELV .003 .008 .181 .369 .731 MKT .000 .007 .005 .011 .992 CAUV -.002 .007 -.132 -.267 .803 a. Dependent Variable: 0200 (Continued)
229 Hourly regression 2009 R0: Continued
Std. Variable B Error Beta t Sig. AHI -.014 .017 -.367 -.789 .474 BD .013 .010 .552 1.326 .256 SOIL .007 .012 .305 .640 .557 SOC -.003 .004 -.296 -.620 .569 ELV .002 .009 .103 .207 .846 MKT .001 .007 .056 .112 .916 CAUV -.003 .007 -.191 -.390 .717 a. Dependent Variable: 0300
Variable Std. B Error Beta t Sig. AHI -.011 .017 -.305 -.640 .557 BD .011 .010 .479 1.091 .337 SOIL .008 .011 .329 .697 .524 SOC -.003 .004 -.377 -.813 .462 ELV .003 .008 .180 .366 .733 MKT -.001 .007 -.038 -.076 .943 CAUV -.004 .007 -.282 -.589 .588 a. Dependent Variable: 0400
Variable Std. B Error Beta t Sig. AHI -.014 .015 -.412 -.905 .417 BD .016 .007 .742 2.214 .091 SOIL .004 .011 .199 .407 .705 SOC -.002 .004 -.303 -.636 .559 ELV .005 .007 .313 .659 .546 MKT -.001 .007 -.070 -.139 .896 CAUV .000 .006 .022 .044 .967 a. Dependent Variable: 500
(Continued)
230 Hourly regression 2009 R0: Continued Variable Std. B Error Beta t Sig. AHI -.015 .016 -.412 -.903 .417 BD .017 .008 .716 2.051 .110 SOIL .006 .011 .267 .553 .610 SOC -.002 .004 -.202 -.411 .702 ELV .003 .008 .185 .376 .726 MKT .001 .007 .104 .210 .844 CAUV .000 .007 -.022 -.044 .967 a. Dependent Variable: 0600
Variable Std. B Error Beta t Sig. AHI - -.014 .014 -.448 1.002 .373 BD .016 .006 .795 2.617 .059 SOIL .005 .010 .227 .467 .665 SOC -.001 .004 -.085 -.171 .872 ELV .003 .007 .183 .372 .729 MKT .002 .006 .192 .391 .716 CAUV .001 .006 .083 .166 .876 a. Dependent Variable: 0700
Variable Std. B Error Beta t Sig. AHI -.013 .013 -.442 -.985 .380 BD .015 .006 .763 2.360 .078 SOIL .003 .010 .159 .321 .764 SOC -.001 .003 -.122 -.247 .817 ELV .004 .007 .324 .684 .531 MKT .001 .006 .073 .146 .891 CAUV .000 .006 .024 .049 .964 a. Dependent Variable: 0800
(Continued) 231 Hourly regression 2009 R0: Continued Variable Std. B Error Beta t Sig. AHI - -.013 .013 -.462 1.042 .356 BD .015 .005 .823 2.902 .044 SOIL .003 .009 .144 .292 .785 SOC -.001 .003 -.174 -.354 .741 ELV .004 .006 .302 .634 .561 MKT .000 .006 .025 .049 .963 CAUV .001 .005 .132 .266 .804 a. Dependent Variable: 0900
Variable Std. B Error Beta t Sig. AHI -.012 .017 -.328 -.695 .525 BD .017 .008 .729 2.132 .100 SOIL .007 .011 .295 .618 .570 SOC -.001 .004 -.122 -.246 .818 ELV .006 .008 .372 .801 .468 MKT .002 .007 .122 .246 .817 CAUV .000 .007 -.036 -.071 .947 a. Dependent Variable: 1000
Variable Std. B Error Beta t Sig. AHI -.003 .020 -.065 -.131 .902 BD .015 .011 .570 1.387 .238 SOIL .014 .011 .522 1.223 .288 SOC .001 .005 .120 .242 .821 ELV .008 .008 .420 .925 .407 MKT .007 .007 .407 .892 .423 CAUV -.003 .008 -.175 -.356 .740 a. Dependent Variable: 1100
(Continued) 232 Hourly regression 2009 R0: Continued Variable Std. B Error Beta t Sig. AHI .001 .021 .032 .065 .951 BD .014 .012 .516 1.205 .294 SOIL .017 .011 .611 1.544 .198 SOC .002 .005 .194 .395 .713 ELV .007 .009 .368 .792 .473 MKT .009 .007 .518 1.210 .293 CAUV -.003 .008 -.168 -.342 .750 a. Dependent Variable: 1200
Variable Std. B Error Beta t Sig. AHI -.001 .018 -.040 -.079 .941 BD .015 .009 .621 1.583 .189 SOIL .014 .010 .582 1.431 .226 SOC .000 .004 -.001 -.002 .999 ELV .007 .008 .433 .961 .391 MKT .005 .007 .327 .691 .528 CAUV -.002 .007 -.114 -.230 .829 a. Dependent Variable: 1300
Variable Std. B Error Beta t Sig. AHI .000 .019 -.012 -.025 .981 BD .015 .010 .582 1.433 .225 SOIL .015 .010 .588 1.455 .219 SOC .002 .005 .178 .362 .736 ELV .006 .008 .357 .765 .487 MKT .008 .007 .503 1.164 .309 CAUV -.001 .007 -.099 -.199 .852 a. Dependent Variable: 1400
(Continued) 233 Hourly regression 2009 R0: Continued Variable Std. B Error Beta t Sig. AHI .001 .015 .037 .073 .945 BD .008 .009 .435 .967 .388 SOIL .011 .008 .582 1.433 .225 SOC .001 .003 .195 .398 .711 ELV .005 .006 .395 .859 .439 MKT .006 .005 .494 1.135 .320 CAUV -.003 .005 -.282 -.589 .588 a. Dependent Variable: 1500
Variable Std. B Error Beta t Sig. AHI -.002 .015 -.079 -.159 .881 BD .009 .008 .475 1.079 .341 SOIL .010 .008 .496 1.143 .317 SOC .002 .003 .243 .502 .642 ELV .004 .007 .298 .625 .566 MKT .006 .005 .538 1.278 .270 CAUV -.003 .005 -.251 -.519 .631 a. Dependent Variable: 1600
Variable Std. B Error Beta t Sig. AHI -.004 .013 -.156 -.316 .768 BD .010 .007 .573 1.400 .234 SOIL .008 .008 .466 1.053 .352 SOC -.001 .003 -.137 -.276 .796 ELV .005 .006 .410 .900 .419 MKT .002 .005 .169 .342 .750 CAUV -.002 .005 -.229 -.471 .662 a. Dependent Variable: 1700
(Continued) 234 Hourly regression 2009 R0: Continued Variable Std. B Error Beta t Sig. AHI -.004 .012 -.176 -.358 .739 BD .011 .006 .698 1.951 .123 SOIL .008 .007 .466 1.053 .352 SOC .000 .003 .070 .141 .895 ELV .004 .006 .349 .745 .497 MKT .004 .005 .373 .805 .466 CAUV .000 .005 -.030 -.061 .954 a. Dependent Variable: 1800
Variable Std. B Error Beta t Sig. AHI -.002 .012 -.067 -.134 .900 BD .011 .006 .693 1.922 .127 SOIL .010 .006 .601 1.505 .207 SOC .000 .003 -.068 -.136 .898 ELV .004 .005 .317 .668 .541 MKT .003 .005 .314 .661 .545 CAUV .000 .005 .032 .064 .952 a. Dependent Variable: 1900
Variable Std. B Error Beta t Sig. AHI -.006 .014 -.210 -.430 .689 BD .012 .007 .630 1.620 .180 SOIL .009 .008 .483 1.104 .332 SOC .001 .003 .099 .199 .852 ELV .001 .007 .085 .170 .873 MKT .006 .005 .490 1.124 .324 CAUV .000 .005 -.032 -.064 .952 a. Dependent Variable: 2000
(Continued) 235 Hourly regression 2009 R0: Continued
Variable Std. B Error Beta t Sig. AHI -.004 .018 -.111 -.222 .835 BD .012 .010 .511 1.189 .300 SOIL .014 .010 .579 1.419 .229 SOC -.001 .004 -.087 -.174 .870 ELV .002 .008 .089 .180 .866 MKT .005 .007 .358 .766 .487 CAUV -.002 .007 -.152 -.308 .774 a. Dependent Variable: 2100
Variable Std. B Error Beta t Sig. AHI -.009 .020 -.217 -.446 .679 BD .013 .011 .508 1.179 .304 SOIL .013 .012 .474 1.078 .342 SOC -.002 .005 -.219 -.448 .677 ELV .002 .009 .093 .187 .860 MKT .003 .008 .200 .408 .704 CAUV -.003 .008 -.210 -.429 .690 a. Dependent Variable: 2200
Variable Std. B Error Beta t Sig. AHI -.010 .020 -.244 -.503 .642 BD .014 .011 .524 1.232 .286 SOIL .012 .012 .450 1.007 .371 SOC -.002 .005 -.253 -.522 .629 ELV .001 .010 .076 .152 .886 MKT .003 .008 .164 .332 .756 CAUV -.003 .008 -.181 -.368 .732 a. Dependent Variable: 2300
236 Hourly regression 2009 R3
Std. Variable B Error t Sig. AHI -.006 .024 -.263 .805 BD .012 .014 .833 .452 SOIL .020 .026 .747 .497 SOC -.001 .006 -.163 .878 ELV .004 .010 .425 .692 MKT .005 .009 .614 .572 CAUV -.008 .008 -.946 .398 a. Dependent Variable: 0000
Std. Variable B Error t Sig. AHI -.008 .021 -.405 .707 BD .014 .012 1.196 .298 SOIL .015 .023 .647 .553 SOC .000 .006 -.021 .984 ELV .004 .009 .412 .702 MKT .005 .008 .660 .545 CAUV -.004 .007 -.593 .585 a. Dependent Variable: 0100
Std. Variable B Error t Sig. AHI -.010 .018 -.533 .623 BD .014 .010 1.445 .222 SOIL .010 .021 .484 .654 SOC -.002 .005 -.309 .773 ELV .003 .008 .357 .739 MKT .002 .007 .319 .766 CAUV -.004 .007 -.581 .592 a. Dependent Variable: 0200 (Continued)
237 Hourly regression 2009 R3: Continued
Std. Variable B Error t Sig. AHI -.013 .019 -.691 .528 BD .014 .011 1.277 .271 SOIL .008 .023 .360 .737 SOC -.001 .005 -.223 .835 ELV .002 .009 .196 .854 MKT .003 .007 .449 .677 CAUV -.005 .007 -.685 .531 a. Dependent Variable: 0300
Std. Variable B Error t Sig. AHI -.011 .019 -.572 .598 BD .012 .011 1.118 .326 SOIL .007 .022 .338 .752 SOC -.002 .005 -.430 .689 ELV .003 .008 .346 .747 MKT .002 .007 .273 .799 CAUV -.006 .007 -.891 .423 a. Dependent Variable: 0400
Std. Variable B Error t Sig. AHI -.011 .017 -.654 .549 BD .018 .008 2.320 .081 SOIL .003 .021 .141 .895 SOC -.001 .005 -.217 .839 ELV .005 .007 .671 .539 MKT .001 .007 .154 .885 CAUV -.001 .007 -.106 .921 a. Dependent Variable: 500 (Continued)
238 Hourly regression 2009 R3: Continued
Std. Variable B Error t Sig. AHI -.013 .019 -.687 .530 BD .017 .009 1.854 .137 SOIL .007 .022 .333 .756 SOC .000 .005 .010 .993 ELV .003 .008 .388 .718 MKT .004 .007 .518 .632 CAUV -.002 .007 -.235 .826 a. Dependent Variable: 0600
Std. Variable B Error t Sig. AHI -.011 .016 -.717 .513 BD .016 .007 2.147 .098 SOIL .006 .019 .311 .771 SOC .001 .004 .261 .807 ELV .003 .007 .401 .709 MKT .004 .006 .682 .533 CAUV .000 .006 .036 .973 a. Dependent Variable: 0700
Std. Variable B Error t Sig. AHI -.010 .015 -.684 .532 BD .015 .007 2.187 .094 SOIL .001 .019 .074 .945 SOC .001 .004 .127 .905 ELV .005 .006 .719 .512 MKT .003 .006 .446 .679 CAUV .000 .006 .039 .970 a. Dependent Variable: 0800 (Continued)
239 Hourly regression 2009 R3: Continued
Std. Variable B Error t Sig. AHI -.011 .015 -.714 .515 BD .016 .006 2.786 .050 SOIL .002 .018 .091 .932 SOC .000 .004 .056 .958 ELV .004 .006 .667 .542 MKT .002 .006 .316 .768 CAUV .001 .006 .183 .864 a. Dependent Variable: 0900
Std. Variable B Error t Sig. AHI -.008 .019 -.435 .686 BD .017 .009 1.802 .146 SOIL .008 .022 .348 .745 SOC .001 .005 .135 .899 ELV .006 .008 .833 .451 MKT .004 .007 .546 .614 CAUV -.001 .007 -.099 .926 a. Dependent Variable: 1000
Std. Variable B Error t Sig. AHI .002 .022 .090 .932 BD .011 .013 .838 .449 SOIL .021 .023 .929 .405 SOC .003 .006 .589 .588 ELV .008 .008 .972 .386 MKT .009 .007 1.217 .290 CAUV -.002 .008 -.262 .807 a. Dependent Variable: 1100 (Continued)
240 Hourly regression 2009 R3: Continued
Std. Variable B Error t Sig. AHI .006 .023 .273 .798 BD .008 .014 .596 .583 SOIL .029 .022 1.305 .262 SOC .004 .006 .760 .490 ELV .007 .009 .836 .450 MKT .010 .007 1.525 .202 CAUV -.002 .008 -.278 .795 a. Dependent Variable: 1200
Std. Variable B Error t Sig. AHI .003 .020 .146 .891 BD .012 .012 1.013 .368 SOIL .023 .020 1.125 .323 SOC .002 .005 .377 .726 ELV .008 .008 .995 .376 MKT .007 .007 .961 .391 CAUV -.002 .007 -.257 .810 a. Dependent Variable: 1300
Std. Variable B Error t Sig. AHI .004 .021 .202 .850 BD .010 .013 .769 .485 SOIL .026 .021 1.264 .275 SOC .004 .005 .758 .491 ELV .007 .008 .811 .463 MKT .010 .007 1.460 .218 CAUV -.001 .008 -.166 .876 a. Dependent Variable: 1400 (Continued)
241 Hourly regression 2009 R3: Continued
Std. Variable B Error t Sig. AHI .004 .016 .255 .811 BD .005 .010 .448 .677 SOIL .018 .016 1.106 .331 SOC .003 .004 .702 .521 ELV .006 .006 .900 .419 MKT .007 .005 1.498 .209 CAUV -.003 .006 -.446 .679 a. Dependent Variable: 1500
Std. Variable B Error t Sig. AHI .000 .016 .019 .986 BD .005 .010 .546 .614 SOIL .015 .017 .895 .421 SOC .003 .004 .839 .449 ELV .004 .006 .668 .541 MKT .008 .005 1.720 .161 CAUV -.002 .006 -.379 .724 a. Dependent Variable: 1600
Std. Variable B Error t Sig. AHI -.002 .014 -.136 .898 BD .009 .008 1.093 .336 SOIL .010 .016 .671 .539 SOC .000 .004 .067 .950 ELV .005 .005 .915 .412 MKT .003 .005 .666 .542 CAUV -.003 .005 -.510 .637 a. Dependent Variable: 1700 (Continued)
242 Hourly regression 2009 R3: Continued
Std. Variable B Error t Sig. AHI -.001 .014 -.108 .919 BD .010 .008 1.266 .274 SOIL .013 .015 .855 .441 SOC .002 .004 .552 .610 ELV .004 .005 .790 .474 MKT .005 .005 1.100 .333 CAUV .000 .005 -.067 .950 a. Dependent Variable: 1800
Std. Variable B Error t Sig. AHI .001 .013 .089 .934 BD .009 .007 1.242 .282 SOIL .017 .013 1.353 .247 SOC .001 .004 .309 .773 ELV .004 .005 .690 .528 MKT .004 .005 .870 .433 CAUV -.001 .005 -.135 .899 a. Dependent Variable: 1900
Std. Variable B Error t Sig. AHI -.004 .015 -.254 .812 BD .009 .009 .994 .377 SOIL .016 .016 1.040 .357 SOC .003 .004 .645 .554 ELV .001 .007 .195 .855 MKT .007 .005 1.457 .219 CAUV -.001 .006 -.229 .830 a. Dependent Variable: 2000
(Continued)
243 Hourly regression 2009 R3: Continued Std. Variable B Error t Sig. AHI -.002 .020 -.122 .909 BD .009 .012 .769 .485 SOIL .025 .020 1.249 .280 SOC .001 .005 .227 .832 ELV .002 .008 .179 .867 MKT .007 .007 1.053 .352 CAUV -.004 .007 -.618 .570 a. Dependent Variable: 2100
Std. Variable B Error t Sig. AHI -.008 .022 -.366 .733 BD .012 .013 .938 .402 SOIL .020 .024 .821 .458 SOC .000 .006 -.052 .961 ELV .002 .009 .177 .868 MKT .006 .008 .723 .509 CAUV -.006 .008 -.771 .484 a. Dependent Variable: 2200
Std. Variable B Error t Sig. AHI -.009 .022 -.423 .694 BD .013 .013 1.024 .364 SOIL .019 .024 .766 .486 SOC -.001 .006 -.112 .916 ELV .001 .009 .140 .896 MKT .005 .008 .637 .559 CAUV -.006 .008 -.736 .502 a. Dependent Variable: 2300
244 Hourly regression 2010 R0
Variable B Std. Error t Sig. AHI -0.024 0.019 -1.262 0.276 BD 0.022 0.01 2.243 0.088 SOIL -0.009 0.014 -0.641 0.556 SOC -0.004 0.005 -0.805 0.466 ELV 0.006 0.01 0.588 0.588 MKT -0.008 0.008 -1.038 0.358 CAUV 0.009 0.008 1.137 0.319 a. Dependent Variable: 0000
Variable B Std. Error t Sig. AHI -0.023 0.019 -1.205 0.295 BD 0.023 0.009 2.557 0.063
SOIL -0.008 0.014 -0.551 0.611 SOC -0.004 0.005 -0.787 0.476 ELV 0.006 0.01 0.572 0.598 MKT -0.008 0.008 -0.956 0.393 CAUV 0.009 0.007 1.318 0.258 a. Dependent Variable: 0100
Variable B Std. Error t Sig. AHI -0.021 0.017 -1.243 0.282 BD 0.019 0.009 2.268 0.086 SOIL -0.009 0.012 -0.699 0.523 SOC -0.003 0.004 -0.598 0.582 ELV 0.006 0.009 0.674 0.537 MKT -0.007 0.007 -0.901 0.418 CAUV 0.008 0.007 1.166 0.308 a. Dependent Variable: 0200 (Continued)
245 Hourly regression 2010 R0: Continued
Variable B Std. Error t Sig. AHI -0.022 0.017 -1.28 0.27 BD 0.019 0.009 2.03 0.112 SOIL -0.009 0.013 -0.75 0.495 SOC -0.003 0.005 -0.695 0.525
ELV 0.006 0.009 0.642 0.556 MKT -0.007 0.007 -1.024 0.364 CAUV 0.007 0.007 1.04 0.357 a. Dependent Variable: 0300
Variable B Std. Error t Sig. AHI -0.023 0.018 -1.271 0.273 BD 0.021 0.01 2.244 0.088 SOIL -0.009 0.014 -0.682 0.533 SOC -0.003 0.005 -0.657 0.547 ELV 0.006 0.01 0.656 0.548 MKT -0.007 0.008 -0.936 0.402 CAUV 0.008 0.007 1.099 0.333 a. Dependent Variable: 0400
Variable B Std. Error t Sig. AHI -0.024 0.019 -1.282 0.269 BD 0.022 0.01 2.215 0.091 SOIL -0.009 0.014 -0.67 0.54 SOC -0.004 0.005 -0.723 0.51 ELV 0.006 0.01 0.63 0.563 MKT -0.008 0.008 -0.983 0.381
CAUV 0.008 0.008 1.075 0.343 a. Dependent Variable: 500 (Continued)
246 Hourly regression 2010 R0: Continued
Variable B Std. Error t Sig. AHI -0.017 0.014 -1.224 0.288 BD 0.017 0.007 2.589 0.061 SOIL -0.006 0.01 -0.589 0.588 SOC -0.001 0.004 -0.282 0.792 ELV 0.006 0.007 0.815 0.461 MKT -0.003 0.006 -0.535 0.621 CAUV 0.006 0.006 1.035 0.359 a. Dependent Variable: 0600
Variable B Std. Error t Sig. AHI -0.013 0.01 -1.217 0.29 BD 0.013 0.005 2.636 0.058 SOIL -0.004 0.008 -0.537 0.62 SOC 0 0.003 -0.195 0.855 ELV 0.004 0.005 0.853 0.442 MKT -0.002 0.005 -0.423 0.694 CAUV 0.004 0.004 0.941 0.4 a. Dependent Variable: 0700
Variable B Std. Error t Sig. AHI -0.011 0.011 -1.054 0.351 BD 0.014 0.003 4.119 0.015 SOIL -0.002 0.008 -0.298 0.78 SOC 0 0.003 -0.063 0.952 ELV 0.004 0.005 0.842 0.447 MKT 0 0.005 -0.176 0.869 CAUV 0.005 0.004 1.323 0.256 a. Dependent Variable: 0800 (Continued)
247 Hourly regression 2010 R0: Continued
Variable B Std. Error t Sig. AHI -0.009 0.01 -0.968 0.388 BD 0.013 0.003 5.032 0.007 SOIL 0.001 0.007 0.162 0.879 SOC 8.15E-05 0.003 0.032 0.976 ELV 0.004 0.005 0.788 0.475 MKT 0.001 0.004 0.241 0.821 CAUV 0.003 0.004 0.803 0.467 a. Dependent Variable: 0900
Variable B Std. Error t Sig. AHI -0.005 0.011 -0.415 0.699 BD 0.011 0.005 2.468 0.069 SOIL 0.006 0.007 0.936 0.402 SOC 0.001 0.003 0.462 0.668 ELV 0.003 0.005 0.537 0.62 MKT 0.005 0.004 1.186 0.301 CAUV 0.001 0.004 0.323 0.763 a. Dependent Variable: 1000
Variable B Std. Error t Sig. AHI -0.007 0.006 -1.107 0.33 BD 0.009 0.002 4.873 0.008 SOIL 8.77E-06 0.005 0.002 0.999 SOC 0 0.002 -0.589 0.588 ELV 0.002 0.003 0.576 0.595 MKT -0.001 0.003 -0.352 0.743 CAUV 0.003 0.002 1.109 0.33 a. Dependent Variable: 1100 (Continued)
248 Hourly regression 2010 R0: Continued
Variable B Std. Error t Sig. AHI -0.004 0.01 -0.399 0.711 BD 0.01 0.005 2.167 0.096 SOIL 0.005 0.007 0.723 0.509 SOC 0.002 0.002 0.809 0.464 ELV 0.003 0.005 0.684 0.531 MKT 0.005 0.004 1.38 0.24 CAUV 0.001 0.004 0.288 0.787 a. Dependent Variable: 1200
Variable B Std. Error t Sig. AHI -0.006 0.01 -0.615 0.572 BD 0.013 0.003 4.766 0.009 SOIL 0.005 0.007 0.718 0.512 SOC 0 0.003 -0.136 0.898 ELV 0.002 0.005 0.442 0.681 MKT 0.002 0.004 0.443 0.681 CAUV 0.003 0.004 0.925 0.408 a. Dependent Variable: 1300
Variable B Std. Error t Sig. AHI -0.004 0.008 -0.523 0.629 BD 0.011 0.002 5.223 0.006 SOIL 0.003 0.006 0.615 0.572 SOC 0.001 0.002 0.35 0.744 ELV 0.002 0.004 0.632 0.562 MKT 0.003 0.003 0.787 0.476 CAUV 0.003 0.003 1.088 0.338 a. Dependent Variable: 1400 (Continued)
249 Hourly regression 2010 R0: Continued
Variable B Std. Error t Sig. AHI -0.005 0.009 -0.536 0.62 BD 0.011 0.003 4.183 0.014 SOIL 0.003 0.006 0.563 0.603 SOC 0.001 0.002 0.508 0.638 ELV 0.003 0.004 0.711 0.516 MKT 0.003 0.003 0.905 0.417 CAUV 0.003 0.003 0.891 0.423 a. Dependent Variable: 1500
Variable B Std. Error t Sig. AHI -0.001 0.009 -0.15 0.888 BD 0.008 0.004 1.891 0.132 SOIL 0.005 0.005 1.007 0.371 SOC 0.002 0.002 0.959 0.392 ELV 0.002 0.004 0.535 0.621 MKT 0.005 0.003 1.817 0.143 CAUV 0.001 0.003 0.385 0.72 a. Dependent Variable: 1600
Variable B Std. Error t Sig. AHI -0.004 0.01 -0.403 0.708 BD 0.011 0.003 3.634 0.022 SOIL 0.005 0.006 0.745 0.498 SOC 0.001 0.002 0.557 0.607 ELV 0.003 0.004 0.619 0.569 MKT 0.004 0.003 1.087 0.338 CAUV 0.003 0.003 0.884 0.427 a. Dependent Variable: 1700 (Continued)
250 Hourly regression 2010 R0: Continued
Variable B Std. Error t Sig. AHI -0.003 0.009 -0.376 0.726 BD 0.009 0.004 2.036 0.111 SOIL 0.003 0.006 0.47 0.663 SOC 0.002 0.002 1.103 0.332 ELV 0.003 0.004 0.808 0.465 MKT 0.004 0.003 1.378 0.24 CAUV 0.002 0.004 0.45 0.676 a. Dependent Variable: 1800
Variable B Std. Error t Sig. AHI -0.005 0.009 -0.568 0.6 BD 0.01 0.004 2.711 0.053 SOIL 0.003 0.006 0.562 0.604 SOC 0.001 0.002 0.608 0.576 ELV 0.003 0.004 0.749 0.496 MKT 0.003 0.003 1.037 0.358 CAUV 0.001 0.004 0.399 0.711 a. Dependent Variable: 1900
Variable B Std. Error t Sig. AHI -0.01 0.009 -1.041 0.357 BD 0.013 0.002 6.711 0.003 SOIL 0 0.007 0.024 0.982 SOC 0 0.002 -0.313 0.77 ELV 0.003 0.005 0.678 0.535 MKT 0 0.004 -0.138 0.897 CAUV 0.004 0.003 1.217 0.29 a. Dependent Variable: 2000
(Continued)
251 Hourly regression 2010 R0: Continued Variable B Std. Error t Sig. AHI -0.019 0.016 -1.171 0.307 BD 0.022 0.005 4.605 0.01 SOIL -0.001 0.012 -0.123 0.908 SOC -0.002 0.004 -0.423 0.694 ELV 0.006 0.008 0.694 0.526 MKT -0.002 0.007 -0.312 0.771 CAUV 0.007 0.006 1.048 0.354 a. Dependent Variable: 2100
Variable B Std. Error t Sig. AHI -0.023 0.019 -1.222 0.289 BD 0.024 0.008 2.973 0.041 SOIL -0.005 0.014 -0.363 0.735 SOC -0.004 0.005 -0.841 0.448 ELV 0.005 0.01 0.547 0.614 MKT -0.007 0.008 -0.828 0.454 CAUV 0.009 0.007 1.185 0.302 a. Dependent Variable: 2200
Variable B Std. Error t Sig. AHI -0.023 0.017 -1.377 0.241 BD 0.019 0.009 2.141 0.099 SOIL -0.008 0.013 -0.597 0.582 SOC -0.003 0.005 -0.707 0.518 ELV 0.006 0.009 0.662 0.544 MKT -0.007 0.008 -0.881 0.428 CAUV 0.006 0.007 0.745 0.498 a. Dependent Variable: 2300
252 Hourly regression 2010 R3
Variable B Std. Error t Sig. AHI -0.02 0.022 -0.892 0.423 BD 0.029 0.006 4.552 0.01 SOIL -0.02 0.026 -0.77 0.485 SOC -0.003 0.006 -0.495 0.647 ELV 0.006 0.01 0.613 0.573 MKT -0.007 0.008 -0.878 0.429 CAUV 0.008 0.008 1.016 0.367 a. Dependent Variable: 0000
Variable B Std. Error t Sig. AHI -0.018 0.022 -0.827 0.455 BD 0.029 0.005 5.532 0.005
SOIL -0.017 0.026 -0.627 0.564 SOC -0.003 0.006 -0.447 0.678 ELV 0.006 0.01 0.599 0.581 MKT -0.007 0.008 -0.831 0.453 CAUV 0.009 0.008 1.124 0.324 a. Dependent Variable: 0100
Variable B Std. Error t Sig. AHI -0.017 0.02 -0.847 0.445 BD 0.025 0.006 4.193 0.014 SOIL -0.019 0.023 -0.828 0.454 SOC -0.002 0.006 -0.332 0.757 ELV 0.006 0.009 0.712 0.516 MKT -0.006 0.008 -0.765 0.487 CAUV 0.008 0.007 1.176 0.305 a. Dependent Variable: 0200 (Continued)
253 Hourly regression 2010 R3: Continued
Variable B Std. Error t Sig. AHI -0.018 0.02 -0.905 0.417 BD 0.026 0.007 3.794 0.019 SOIL -0.022 0.023 -0.917 0.411
SOC -0.003 0.006 -0.438 0.684 ELV 0.006 0.009 0.673 0.538 MKT -0.007 0.008 -0.858 0.439 CAUV 0.008 0.007 1.033 0.36 a. Dependent Variable: 0300
Variable B Std. Error t Sig. AHI -0.019 0.022 -0.88 0.429 BD 0.028 0.007 4.247 0.013 SOIL -0.021 0.026 -0.827 0.455 SOC -0.002 0.006 -0.381 0.723 ELV 0.007 0.01 0.691 0.528 MKT -0.007 0.008 -0.781 0.478 CAUV 0.008 0.008 1.08 0.341 a. Dependent Variable: 0400
Variable B Std. Error t Sig. AHI -0.02 0.022 -0.9 0.419 BD 0.029 0.007 4.299 0.013 SOIL -0.021 0.026 -0.819 0.459
SOC -0.003 0.006 -0.435 0.686 ELV 0.007 0.01 0.661 0.545 MKT -0.007 0.009 -0.818 0.459 CAUV 0.008 0.008 1.021 0.365 a. Dependent Variable: 500 (Continued)
254 Hourly regression 2010 R3: Continued
Variable B Std. Error t Sig. AHI -0.013 0.017 -0.783 0.477 BD 0.021 0.005 3.909 0.017 SOIL -0.014 0.02 -0.728 0.507 SOC 0 0.005 -0.026 0.98 ELV 0.006 0.007 0.877 0.43 MKT -0.003 0.007 -0.401 0.709 CAUV 0.007 0.006 1.218 0.29 a. Dependent Variable: 0600
Variable B Std. Error t Sig. AHI -0.01 0.013 -0.77 0.484 BD 0.015 0.004 3.666 0.021 SOIL -0.01 0.015 -0.688 0.529 SOC 0 0.004 0.061 0.954
ELV 0.005 0.005 0.921 0.409 MKT -0.001 0.005 -0.279 0.794 CAUV 0.005 0.004 1.164 0.309 a. Dependent Variable: 0700
Variable B Std. Error t Sig. AHI -0.007 0.013 -0.589 0.587 BD 0.016 0.003 4.826 0.008 SOIL -0.005 0.015 -0.331 0.757 SOC 0.001 0.004 0.266 0.804 ELV 0.005 0.005 0.921 0.409 MKT 0 0.005 -0.093 0.931 CAUV 0.006 0.004 1.501 0.208 a. Dependent Variable: 0800 (Continued)
255 Hourly regression 2010 R3: Continued
Variable B Std. Error t Sig. AHI -0.006 0.011 -0.54 0.618 BD 0.013 0.004 3.637 0.022 SOIL 0.001 0.014 0.089 0.934 SOC 0.001 0.003 0.447 0.678
ELV 0.004 0.005 0.86 0.438 MKT 0.002 0.004 0.405 0.706 CAUV 0.003 0.004 0.857 0.44 a. Dependent Variable: 0900
Variable B Std. Error t Sig. AHI -0.001 0.012 -0.119 0.911 BD 0.009 0.006 1.392 0.236 SOIL 0.012 0.013 0.932 0.404 SOC 0.003 0.003 0.967 0.388 ELV 0.003 0.005 0.597 0.582 MKT 0.005 0.004 1.422 0.228 CAUV 0.001 0.004 0.322 0.764 a. Dependent Variable: 1000
Variable B Std. Error t Sig. AHI -0.005 0.008 -0.709 0.517 BD 0.01 0.001 8.668 0.001 SOIL 0 0.009 -0.05 0.962 SOC 0 0.002 -0.139 0.896 ELV 0.002 0.003 0.609 0.575
MKT 0 0.003 -0.208 0.845 CAUV 0.002 0.003 0.842 0.447 a. Dependent Variable: 1100 (Continued)
256 Hourly regression 2010 R3: Continued
Variable B Std. Error t Sig. AHI 0 0.012 -0.077 0.942 BD 0.008 0.006 1.201 0.296 SOIL 0.009 0.013 0.702 0.521 SOC 0.003 0.003 1.318 0.258 ELV 0.004 0.005 0.769 0.485 MKT 0.006 0.003 1.626 0.179 CAUV 0.002 0.004 0.485 0.653 a. Dependent Variable: 1200
Variable B Std. Error t Sig. AHI -0.003 0.012 -0.288 0.788 BD 0.012 0.004 2.789 0.049 SOIL 0.01 0.013 0.792 0.472 SOC 0.001 0.003 0.373 0.728 ELV 0.002 0.005 0.481 0.656
MKT 0.002 0.004 0.55 0.611 CAUV 0.003 0.004 0.616 0.571 a. Dependent Variable: 1300
Variable B Std. Error t Sig. AHI -0.001 0.01 -0.138 0.897 BD 0.01 0.004 2.416 0.073 SOIL 0.008 0.01 0.735 0.503 SOC 0.002 0.002 0.872 0.432 ELV 0.003 0.004 0.706 0.519 MKT 0.003 0.003 0.847 0.445 CAUV 0.003 0.003 1.036 0.359 a. Dependent Variable: 1400 (Continued)
257 Hourly regression 2010 R3: Continued Variable B Std. Error t Sig. AHI -0.002 0.01 -0.147 0.89 BD 0.01 0.005 2.123 0.101 SOIL 0.007 0.012 0.634 0.561 SOC 0.003 0.003 1.027 0.363 ELV 0.003 0.004 0.797 0.47 MKT 0.004 0.004 1.001 0.374
CAUV 0.003 0.003 0.979 0.383 a. Dependent Variable: 1500
Variable B Std. Error t Sig. AHI 0.001 0.009 0.15 0.888 BD 0.005 0.006 0.918 0.41 SOIL 0.011 0.009 1.13 0.322 SOC 0.003 0.002 1.55 0.196 ELV 0.002 0.004 0.612 0.574 MKT 0.005 0.003 1.964 0.121 CAUV 0.002 0.003 0.51 0.637 a. Dependent Variable: 1600
Variable B Std. Error t Sig. AHI 0 0.011 -0.038 0.972 BD 0.009 0.005 1.818 0.143 SOIL 0.01 0.011 0.876 0.43 SOC 0.003 0.003 1.109 0.329 ELV 0.003 0.004 0.698 0.524 MKT 0.004 0.004 1.157 0.312
CAUV 0.003 0.004 0.906 0.416 a. Dependent Variable: 1700 (Continued)
258 Hourly regression 2010 R3: Continued
Variable B Std. Error t Sig. AHI 0 0.01 -0.022 0.983 BD 0.007 0.006 1.141 0.317 SOIL 0.006 0.012 0.48 0.656 SOC 0.003 0.002 1.612 0.182 ELV 0.004 0.004 0.917 0.411 MKT 0.005 0.003 1.525 0.202 CAUV 0.003 0.004 0.827 0.455 a. Dependent Variable: 1800
Variable B Std. Error t Sig. AHI -0.002 0.01 -0.216 0.839 BD 0.008 0.005 1.606 0.184 SOIL 0.006 0.011 0.513 0.635 SOC 0.003 0.002 1.083 0.34 ELV 0.003 0.004 0.834 0.451 MKT 0.004 0.003 1.259 0.276 CAUV 0.002 0.004 0.58 0.593 a. Dependent Variable: 1900
Variable B Std. Error t Sig. AHI -0.007 0.011 -0.609 0.576 BD 0.014 0.002 7.701 0.002 SOIL - 0.013 0 1 7.35E- 06 SOC 0 0.003 0.114 0.914 ELV 0.003 0.005 0.731 0.506 MKT - 0.004 -0.022 0.984 9.33E- 05 CAUV 0.004 0.004 1.063 0.348 a. Dependent Variable: 2000 (Continued)
259 Hourly regression 2010 R3: Continued
Variable B Std. Error t Sig. AHI -0.014 0.019 -0.736 0.503 BD 0.025 0.003 7.416 0.002 SOIL -0.005 0.023 -0.206 0.847 SOC 0 0.005 -0.024 0.982 ELV 0.006 0.008 0.741 0.5 MKT -0.001 0.008 -0.161 0.88 CAUV 0.006 0.007 0.943 0.399 a. Dependent Variable: 2100
Variable B Std. Error t Sig. AHI -0.019 0.022 -0.843 0.446 BD 0.03 0.004 7.106 0.002 SOIL -0.012 0.027 -0.447 0.678 SOC -0.003 0.006 -0.44 0.683 ELV 0.006 0.01 0.57 0.599 MKT -0.006 0.009 -0.675 0.537 CAUV 0.007 0.008 0.927 0.406 a. Dependent Variable: 2200
Variable B Std. Error t Sig. AHI -0.02 0.02 -0.994 0.376 BD 0.026 0.007 3.859 0.018 SOIL -0.02 0.024 -0.828 0.454 SOC -0.002 0.006 -0.419 0.697 ELV 0.006 0.009 0.691 0.527 MKT -0.005 0.008 -0.652 0.55 CAUV 0.006 0.008 0.736 0.503 a. Dependent Variable: 2300
260