1. Abstract
Monitoring river health is important to determine if urban expansion, intensifying agricultural
activities and other human land-uses are impacting river systems and if so what influential
mechanisms are involved. This is important because there is still some limited understanding
on what causes river degradation and how to effectively mitigate the resultant problems. To
provide a complete health assessment and comprehensive understanding of how degradation
can be caused, the monitoring of both structural and functional health indicators are required.
This investigation quantified the relationship between land-uses and ecological integrity from
five sites in the Maitai River. Macroinvertebrate communities were utilized as the
investigations structural health indicator. Whereby, six Surber samples at each of the five sites
was collected, enabling the calculation and evaluation of the MCI, QMCI and EPTtaxa
(Ephemeroptera, Plecoptera, Trichoptera) health indices. The functional health indicators used
in this investigation included ecosystem metabolism (ecosystem respiration and gross primary
production) measured with single stationed D-opto loggers, and organic matter decomposition
measured with standardised cotton strips. Ecosystem metabolism was calculated by using the
night-time regression method, whilst organic matter decomposition was determined by
measuring the tensile strength loss in each cotton strip after a 22-30 day deployment period.
The monitoring of the Maitai River in New Zealand was particularly important because it
provided a distinct land use gradient, with a pristine site at its headwaters down to a highly
urbanised low-land river reach.
The Maitai Rivers macroinvertebrate community indicated that pollution tolerant taxa are
more abundant and prolific when moving longitudinally downriver. Additionally, ecosystem
metabolism and organic matter decomposition both increased when moving downriver; with
increases indicative of a decreasing river health. As a result it was concluded that increases in
human land-uses longitudinally downriver, particularly urbanisation and exotic vegetation,
caused extensive health reductions in the Maitai River.

2. Contents Pages
1.0 Introduction 1-20
1.1 River Health 1-3
1.2 Catchment Land-use and River Health 4-6
1.3 Monitoring River Health 7-10
1.3.1 Macroinvertebrates 11-12
1.3.2 Ecosystem Metabolism 13-16
1.3.3 Organic Matter Decomposition 17-19
1.4 Maitai River and Research Objectives 20
2.0 Materials and Methods 21-32
2.1 Study Area and Sites 21-25
2.2 Periphyton Cover 26
2.3 Environmental Variables 27
2.3.1 Nutrient Concentrations 27
2.3.2 Physiochemical Variables 27
2.4 Macroinvertebrates 28
2.5 Cotton Strip Assay 29
2.6 Metabolism 30
2.7 Data Analysis 31
2.7.1 Metabolism 31
2.7.2 Organic Matter Decomposition 32
2.7.3 Macroinvertebrate Communities 32
3.0 Results 33-60
3.1 Metabolism 33
3.1.1 Metabolism Spatial Patterns 33-37
3.1.2 Relationships Between Metabolism, Physicochemical and Biological
Variables 38
3.1.3 Metabolism River Health Results 39-44
3.2 Cotton Strip Assays 45
3.2.1 Cotton Strip Assay Spatial Patterns 46

3. 3.2.2 Cotton Strip Assay Temporal Patterns 47
3.2.3 Relationships Between Cotton Strip Assay, Physicochemical
and Biological Variables 48
3.2.4 Cotton Strip Assay Health Results 48
3.3 Macroinvertebrate Community Compositions 49-50
3.3.1 Macroinvertebrate Indices 51
3.3.2 MCI Scores 52
3.3.3 QMCI Scores 53
3.3.4 Percentage EPTTaxa
(Ephemeroptera, Plecoptera, Trichoptera Taxa) 54
3.3.5 Density 55
3.3.6 Macroinvertebrate Health Results 56-57
3.3.7 Relationships Between Macroinvertebrate Communities and
Physicochemical Variables 58
3.4 The Disturbance Gradient 59-60
4.0 Discussion 61-68
4.1 Metabolism 61-63
4.2 Organic Matter Decomposition 64
4.3 Macroinvertebrate Communities 65-67
5.0 Concluding Remarks 68-69
5.1 Investigation Limitations and Further Research 68-69
5.2 Conclusion 69
References 70-77
Appendices 78-80
Acknowledgements 80

4. Figures Pages
Figure 1.1- Threats to river ecosystems and the subsequent ecosystem
services effected (Giller 2005). 3
Figure 1.2- A healthy river ecosystem before urbanisation and the
impact imposed onto a river ecosystem after urbanisation development
completion (modified from Paul and Meyer. 2001). 5
Figure 1.3- A healthy river ecosystem before forestry activity and the
impact imposed onto a river ecosystem during/after forest activity
(modified from Smith & Owens 2014; Johnson et al. 2005). 6
Figure 1.4- Drivers of Gross Primary Production and Ecosystem
metabolism that can cascade down to river function at a patch
scale (Yates et al. 2013). 16
Figure 1.5- Alterations of two physio-chemical factors and the
overall impact, in regard to OMD, when riparian vegetation is
removed by human activities (i.e. land uses) (modified from
Vysna et al. 2014; Collier et al. 2013; Woodward et al. 2012;
Hopkins et al. 2011; Clapcott et al. 2010; Clapcott & Barmuta
2010; Young et al. 2008; Uehlinger 2006). 19
Figure 2.1- All of the sites used to assess river health in the Maitai
River; from the highest site at the South Branch down to the lowest
sites at Avon Terrace (Land Information New Zealand Data Service). 23
Figure 2.2- Three of the sites used to investigate river health,
1 indicates a downstream viewpoint whilst 2 shows a upstream view
point at each site; (A) shows the South Branch, (B) shows Site B and
(C)is at the Campground site. 24
Figure 2.3- Three of the sites used to investigate river health,
1 indicates a downstream viewpoint whilst 2 shows a upstream
view point at each site; (D) shows the Golf course, (E) shows
Dennes Hole and (F ) is at Avon Terrace. 25

5. Figure 2.4- A Periphyton viewer being used in the field to estimate
fine sediment, green algae, diatoms and cyanobacterial cover. 26
Figure 2.5–The use of a turbidity meter to measure turbidity. 27
Figure 2.6- Part of the macroinvertebrate identification process,
(A) depicts the sieves used to separate the organic matter from
the invertebrates, whilst, (B and C) show two samples of
macroinvertebrates in petri dishes ready for microscopic analysis. 28
Figure 2.7– Cotton strips used to measure OMD; before deployment
(A), and after approximately ~30 days of deployment (B). 29
Figure 3.1- The transition between an autotrophic and a
heterotrophic system; (A) South Branch, (B) Site B, from
Nov 2014-Feb 2015. 34
Figure 3.2- The transition between an autotrophic and a
heterotrophic system; (C) campground, (D) Dennes Hole, (E) Avon
Terrace from Oct 2014-Feb 2015. 35
Figure 3.3- A Comparison between the lower 3 sites for GPP and ER;
Avon Terrace=1, Dennes Hole=2, Campground=3. 36
Figure 3.4- A Comparison of GPP for all five sites; 1.0 Avon Terrace,
2.0 Dennes Hole, 3.0 The Campground, 4.0 and 5.0 Site B. 37
Figure 3.5- The temporal and spatial variation in health values for
the lower 3 site; (A) Ecosystem Respiration and (B) gross primary
production (Parkyn et al. 2010). 39
Figure 3.6- The relationship between the P:R and flow at Avon
Terrace in relation to the P:R ratio health boundaries (Avon Terrace
was the only site with accurate and accessible flow data)
(Parkyn et al. 2010). 40
Figure 3.7- Health transitions from Oct 2014-Feb 2015 at sites
(A) South Branch and (B) Site B (Parkyn et al. 2010). 42
Figure 3.8- Health transitions from Oct 2014-Feb 2015 at sites
(C) Campground and (D) Dennes Hole (Parkyn et al. 2010). 43
Figure 3.9- Health transitions from Oct 2014-Feb 2015 at Avon Terrace
(Parkyn et al. 2010). 44
Figure 3.10- The %CTSL per dday at each of the five sites; (1) Avon Terrace,
(2) Dennes Hole, (3) Campground, (4) Site B, (5) South Branch. 46

6. Figure 3.11- Relationship between flow and organic matter decomposition
where high flood events in the Maitai River may increase organic
matter decomposition rates; accurate flow data was only accessible
for Avon Terrace. 47
Figure 3.12- The number of individuals of pollution sensitive
macroinvertebrate species and other species (generally pollution
tolerant) found at Avon Terrace, Dennes Hole, The Campground,
Site B and The South Branch in the Maitai River (E=Ephemeroptera,
P=Plecoptera, T=Trichoptera). 49
Figure 3.13- The proportion of different functional feeding groups
at each site, from the Maitai River on the 3rd and 4th of March 2015;
(A) Avon Terrace, (B) Dennes Hole, (C) Campground, (D) Site B and
(E) the South Branch (Winterbourn 2000; Jaarsma et al. 1998;
Winterbourn 1996; Lester et. al. 1994; Carver et al. 1991;
Colless & McAlpine 1991; Greenslade 1991; Quinn & Hickey 1990;
Chadderton 1988; Winterbourn et al. 1984; Winterbourn & Mason 1983;
Cowie 1980; Winterbourn 1980; Cowley 1978). 50
Figure 3.14- The MCI scores at five sites investigated from the Maitai River
(with 25th interquartile range). 52
Figure 3.15- The QMCI scores at five sites investigated from the Maitai River
(with 25th interquartile range). 53
Figure 3.16- The percentage of EPTTaxa at five sites investigated from the
Maitai River (with 25th interquartile range). 54
Figure 3.17- The densities of macroinvertebrates at five sites from the
Maitai River (with 25th interquartile range). 55
Figure 3.18- The different numbers of pollution sensitive taxa individuals
and species at each site in the Maitai River; E=Ephemeroptera,
P=Plecoptera, T=Trichoptera (excluding Oxyethira albiceps, a pollution
tolerant Trichoptera). 56
Figure 3.19- The health of each site in regard to macroinvertebrate
community (A) MCI scores and (B) QMCI scores. 57
Figure 3.20- The strongest correlations between dissolved inorganic
nitrogen, fine sediment and temperature with specific macroinvertebrate
health indices; (A) Ephemeroptera, Plecoptera and trichoptera taxa and
dissolved inorganic nitrogen, (B) MCI and fine sediment, (C) QMCI and
fine sediment and (D) Density and temperature. 58
Figure 3.21- Land uses that surround each site; (A) Avon Terrace,
(B) Dennes Hole, (C) Campground, (D) Site B, (E) South Branch. 59

7. Figure 3.22- The relationships between river health indicators and the
proportion of human disturbance (pasture, urban and exotic vegetation);
(A) %EPT taxa, (B) GPP and ER and (C) OMD. 60

8. Tables Pages
Table 1.1- Variations in river characteristics at headwaters, mid reaches and
lower reaches, described by the River Continuum Concept
(Vannote et al. 1980). 8
Table 1.2- The health ranges of different health categories for cotton
strip decomposition, gross primary production and ecosystem respiration
(Young et al. 2008 in Parkyn et al. 2010). 10
Table 1.3- The health ranges of different health categories for
macroinvertebrate community index and the quantitative macroinvertebrate
community index (Stark & Maxted 2007). 10
Table 1.4- How different factors can influence metabolism within a
river ecosystem. 15
Table 2.1- Study site descriptions longitudinally along the Maitai River
(Mills 2015.Unpublished). 22
Table 3.1- The ranges and averages of GPP and ER at all the five sites along
the Maitai River. 33
Table 3.2- The correlations of GPP and ER with biological and physicochemical
variables. 38
Table 3.3- The health values found at Site B and The South Branch from
Nov 2014 and Feb 2015. 41
Table 3.4- The health boundaries for P:R, GPP and ER (Parkyn et al. 2010). 41
Table 3.5- The ranges for the k-coefficients kd-1 (divided by deployment days),
kdd-1 (divided by deployment days and temperature) and %CTSL for each
cotton collection date at each site from the Maitai River. 45
Table 3.6- The relationships between OMD (%CTSL), biological and
physicochemical variables. 48
Table 3.7- The correlations between OMD (kd-1 and kdd-1), biological
and physicochemical variables. 48

9. Table 3.8- The macroinvertebrate indices of MCI, QMCI, EPTtaxa and Density
calculated for Avon Terrace, Dennes Hole, The Campground, Site B and the
South Branch, including each replicate value and mean value. 51
Table 3.9- Health boundaries in regard to MCI Scores and QMCI score values. 57
Table 7.1- An example of the macro-invertebrate collection location procedure
(from the South Branch site). 78
Table 7.2- The number of individuals from each taxonomic group, found at each
of the five Maitai River sites. 79-80

10. 1
1.0 Introduction
1.1 River Health
Previously there has been scientific contention and debate on the use of the term
‘health’ within river investigations. It was stated that it cannot be observed and thus was
inappropriate (Scrimgeour & Wicklum 1996; Suter 1993; Calow 1992). But, the terms
acceptance has increased in recent years, a result of objective and empirically verified
structural and functional health indicator developments. However, the perspective of river
health does vary in society depending on the user (Boulton 1999; Karr 1999). Freshwater
scientists term health from ecological integrity, functional processes and water quality.
Industry term a river healthy if the quantity of water is sufficient for business requirements.
Whereas, recreational fishing groups determine river health by harvest quantity and quality
(Karr 1999). From a scientific perspective, ‘health’ can be regarded as a complex term
because it encompasses the combination of biological, chemical and physical characteristics
(Barbour et al. 2000). These characteristics vary depending on numerous factors including
geographical latitude (i.e. climate) and altitude, catchment land uses, geological presence and
hydrological characteristics. In regard to this every river has unique physical, chemical and
biological characteristics. However, similarly all rivers contain organic carbon, an essential
component for life, acquired from allochthonous (external) or autochthonous (internal) carbon
sources (Vannote et al. 1980). The presence of organic carbon in rivers enables river health to
be conceptualised, understood and monitored because it facilitates the measurements of
structural (i.e. river communities) and functional (i.e. river processes) river responses.
Globally, dam constructions, water abstractions and surrounding land uses (e.g. agriculture,
urbanisation and forestry) are unsustainably exploiting river ecosystems (Jorda-Capdevila &
Rodriguez-Labajos 2015; Ward & Stanford 1995), causing significant degradation threats to
numerous river ecosystems (Giller 2005) (see Figure 1.1). Human induced alterations from
resource exploitations cause changes to vital river components and processes, including
nutrient cycles, organic matter decomposition, biological communities and hydrological
regimes (Jorda-Capdevila & Rodriguez-Labajos 2015; Poff et al. 1997), leading to global
river degradation (Alvarez-Cabria et al. 2010; Vugteveen et al. 2006; Blakely & Harding
2005; Brooks et al. 2002). River exploitation is driven by water demands for human uses
(Boulton 1999). Water is essential for life so there is no surprise that humans require water for
consumption and crop growth. However, there are two issues that are growing in concern.

11. 2
Firstly, excessive quantities of water required for human uses are conflicting with river
ecosystem requirements causing severe ecological damage and river degradation. This is a
detrimental pattern being found in many countries including New Zealand (Poff et al. 2003;
Robson 2002 in Poff et al. 2003). Secondly, rises in global populations are causing further
increases in water resource demands and land use intensities (Poff et al. 2003; Zalidis et al.
2002). As a consequence there is a high risk of causing irreversible damage to freshwater
ecosystems in the near future. This is of growing concern because it has been identified that
previous and current freshwater exploitation has already resulted in 65% of global habitats
(related to freshwater discharges) to be highly at risk of severe damage (Vӧrӧsmarty et al.
2010). But, as a result of degradation evidence many countries (including New Zealand,
South Africa, Australia, North America and Europe) have accepted that degradation
mitigation and further pollution prevention is important, to facilitate river ecosystem health
improvements (Poff et al. 2003). However, an acceptable level of pollution must be defined to
avoid the constriction and reduction of economic development (Hickey & Walker 1995). The
enthusiasm and actions of many countries aiming to prevent pollution in river ecosystems
indicates that society has obtained the realisation that once river environments become
irreversibly damaged, irrevocable impacts will be extended towards human society and
economic development (Gessner & Chavet 2002). Previous realisations of pollution risks
were subjective causing the widespread use of the precautionary principle, but more recently
pollution realisations and actions are a result of objective evidence. Quantitative river
degradation evidence is important because it facilitates compliance from governments and
other stakeholders to implement mitigation and rehabilitation strategies (Hickey & Walker
1995). Alongside this, objective evidence also improves the communication of ecosystem
health to communities, therefore increasing pollution awareness.

12. 3
Figure 1.1- Threats to river ecosystems and the subsequent ecosystem services effected (Giller 2005).

13. 4
1.2 Catchment Land-use and River Health
Land-use is increasing in intensification and over larger spatial areas at a global scale.
Catchment land-uses and subsequent human activities can cause detrimental physical,
chemical and biological alterations to river ecosystems. Consequently, structural communities
(e.g. macroinvertebrates, aquatic plants) and functional processes (e.g. metabolism, organic
matter decomposition) can become modified leading to river health abatement (Miserendino
& Prinzio 2008). However, activities and subsequent impacts will vary between different
land-uses (e.g. urbanisation and agriculture; see Figure 1.2 and Figure 1.3) and levels of
intensity.
The monitoring of land-use is essential for both human and ecological integrity. Although, it
is frequently perceived that humans are separate to the natural environment (Pepper 1999),
humans still require ecosystem services to survive. The main problem is that once humans
begin to experience the effects of unsustainable land-use activities, for instance soil depletions
that cause reductions in crop yields, the subsequent damage imposed onto ecosystems
becomes difficult to reverse. This is why it is important to monitor structural and functional
river responses (i.e. health indicators) in relation to a land-use gradient. As understanding can
facilitate early river degradation diagnosis and prevent irreversible ecosystem damage. This
enables mitigation and rehabilitation strategies to be acted on early before extreme health
reductions are caused (i.e. poor health).

14. 5
Figure 1.2- Diagram
5

15. 6
Figure 1.3- Diagram
6

16. 7
1.3 Monitoring River Health
Monitoring river health is important to identify the impact of human disturbances on river
ecosystems and organisms. In regard to this, sites are recommended to follow a gradient of
disturbance, with different human influences and varying land-use intensities (Stoddard et al.
2006; Allan 2004; Stein et al. 2002; Boulton 1999). A gradient of disturbance can be essential
when assessing river health (from pristine undisturbed sites to unhealthy highly disturbed
sites) as it improves understandings of how structural and functional indicators respond to
different human influences, aiding in river rehabilitation decisions (Karr 1999). Investigations
must be aware that natural disturbances (e.g. seasonal flooding and droughts) also develop
resulting in river condition alterations. However, natural disturbances are still indicative of
healthy river ecosystems as they can have an essential role in the connectivity of energy and
nutrients and the life stages of macroinvertebrates (Ward 1998). Alongside this, the ‘flood
pulse concept’ (Junk et al. 1989) and the ‘natural flow paradigm concept’ (Poff et al. 1997)
previously stated that river flow is a main controller of internal river conditions, suggesting
that flow has a high influence on functional and structural health indicators. These concepts
complement the recommendations for a disturbance gradient because flow can be altered
considerably by numerous human activities at varying intensities. The ‘flood pulse concept’
also specifically stated that there are strong interactions between rivers and their floodplains,
again suggesting that human activities (on floodplains) can directly impact river ecosystems.
The ideal investigative scenario to assess river health (based on the ‘reference condition
concept’; Stoddard et al. 2006) would be the inclusion of a pristine site that exhibits the rivers
natural state (Young et al. 2008), but, this can be unachievable. Consequently, determining
the extent of river degradation can be difficult as accurate comparisons between healthy
conditions (undisturbed sites) and unhealthy (highly human influenced) conditions are limited
(Boulton 1999). In response to this, if no pristine sites are present within a river health
investigation the river continuum concept (RCC) can be used as a baseline to determine the
basic characteristics that should be present at headwaters, mid-reaches and lower reaches
(Vannote et al. 1980) (Table 1.1). The RCC also expresses that structural and functional river
characteristics interact and adjust to each other (Vannote et al. 1980); indicating that upstream
reach alterations from human influences can cascade down to lower reaches, extending the
area of impact (Malmqvist & Rundle 2002). In regard to this river health monitoring should
consist of numerous sites at headwaters, intermediate reaches and lower reaches to account
for all possible influences and the longitudinal transfer of impacts.

17. 8
Table 1.1 – Variations in river characteristics at headwaters, mid reaches and lower reaches,described by the
River Continuum Concept (Vannote et al. 1980).
River
Characteristic
Headwaters Mid-Reaches Lower Reaches
Channel
characteristics
Narrow,deep channel Wider, deep Very wide, deep
Macroinvertebrate
Communities
Dominated by
shredders and
collectors
Dominated by grazers
and collectors
Dominated by
collectors
Riparian vegetation
presence
Yes- highly shaded Lower- more sunlight
and sediment available
Considerably less
riparian vegetation
Gradient Steep Lower gradient Low gradient
Flow Fast More pools present,
slower flow
Slow
P/R Nature Heterotrophic Autotrophic Heterotrophic
Species richness Medium Highest- a result of
optimal temperatures
Medium
To provide a comprehensive investigation that identifies highly accurate evidence and
subsequently a holistic river health assessment (i.e. ecological integrity), both structural and
functional health indicators are required (Stoddard et al. 2006; Karr 1999). Traditionally only
structural health indicators (i.e. Macro invertebrate, fish and plant communities) were used
within health investigations (Yates et al. 2014; Yates at al. 2013; Bunn et al. 2010; Young et
al. 2008). Subsequently, an incomplete assessment and evaluation of a rivers ecological
integrity would be the result (Clapcott et al. 2010; Silva-Junior et al. 2014). To achieve a
complete evaluation of a rivers ecological health both traditional structural health indicators
and more recent functional health indicators (i.e. metabolism, nutrient cycling, organic matter
processing) should be measured and monitored (Collier et al. 2013; Li et al. 2013; Clapcott et
al. 2010; Young et al. 2008). Functional indicators are particularly beneficial because they can
act as early warnings of degradation (Boulton 1999; Bunn et al. 1999). Additionally,
measuring both structural and functional indicators is essential as they have been shown to
respond differently to impacts at and from different scales (i.e. catchment, reach and patch
scales) (Bunn et al. 2010; Clapcott et al. 2010; Young & Collier 2009; Young et al. 2008.).
Structural indicators have been suggested to be more influenced by landscape factors
(catchment scale), whilst, functional indicators are more affected by stream position in
relation to the availability of abiotic factors (reach and patch scale), i.e. light, nutrients,
sediment and flow (Yates et al. 2014).

18. 9
While concordance can confirm degraded river health, investigating various responses
exhibited by both structural and functional health indicators can help identify specific impacts
and determine management actions (e.g. Young & Collier 2009). This is particularly
important because every river will respond differently to natural and human disturbances, a
consequence of the varying characteristics that encompass every rivers surrounding catchment
(Wood & Armitage 1997). Additionally, health boundaries have been developed for both
structural and functional indicators to aid in scientific studies; which are especially beneficial
when an investigation has no access to a pristine, healthy reference site (Table 1.2 and Table
1.3). Furthermore, it has also been identified that river health research has commonly be
conducted over larger spatial areas (Clapcott et al. 2012), indicating that research on a small
spatial scale (focused on one river) is limited and therefore required.

19. 10
Table 1.2- The health ranges of different health categories for cotton strip decomposition, gross primary
production and ecosystemrespiration (Young et al. 2008 in Parkyn et al. 2010).
Health category Cotton Strip
Decomposition
(kd-1)
Gross Primary
Production (GPP)
(gO2/m2/day)
Ecosystem
Respiration (ER)
(gO2/m2/day)
Healthy 0.05-0.17 <4.0 1.5-5.5
Satisfactory 0-0.05 & 0.17-0.37 4.0-8.0 0.7-1.5 or 5.5-10.0
Poor 0.37-0.60 >8.0 <0.7 or >10.0
Table 1.3- The health ranges of different health categories for macroinvertebrate community index and the
quantitative macroinvertebrate community index (Stark & Maxted 2007).
Health Category Macroinvertebrate
Community Index (MCI)
Quantitative
Macroinvertebrate
Community Index (QMCI)
Excellent >120 >6.00
Good 100-119 5.00-5.99
Fair 80-99 4.00-4.99
Poor <80 <4.00

20. 11
1.3.1 Macroinvertebrates
Macroinvertebrate communities (i.e. a structural community) have been used as successful
indicators of river health globally (Yazdian et al. 2014; Alvarez-Cabria et al. 2010; Young et
al. 2008). Widespread use of macroinvertebrates is a consequence of fairly easily attainable
river health results (even though sample collection is more difficult). This is achieved
(specifically in New Zealand) with the use of the macroinvertebrate community index (MCI)
that uses presence-absence data and the quantitative macroinvertebrate community index
(QMCI) which utilizes quantitative data; pollution scores are designated to macroinvertebrate
genera and families (Stark 1998; Stark 1993). Health assessments and thus pollution
abatement decisions can be achieved by using MCI and QMCI scores as the identification and
proportions of taxa that have pollution tolerance or intolerance can be fairly straightforward.
A score of one expresses extremely pollution tolerant taxa, whilst, a score of ten indicates
extremely pollution intolerant taxa (Stark 1998; Stark 1993). However, there has been debate
in the literature on which pollution score index (MCI or QMCI) is the most sensitive to
pollution and subsequently the most appropriate for river health assessments (e.g. Scarsbrook
et al. 2000; Lester et al. 1994; Quinn & Hickey 1990). MCI and QMCI results, from the same
investigation, can have inconsistent results (Wright-Stow & Winterbourn 2003). In response
to this percentage Ephemeroptera, Plecoptera and Trichoptera taxa richness (%EPTtaxa) can
also be used to compliment MCI and QMCI scores. All three macroinvertebrate orders within
the %EPTtaxa are highly pollution sensitive; whereby a high %EPTtaxa would indicate a high
river health.
But, achieving a holistic macroinvertebrate community data set that represents a whole river
system to aid in river health evaluations can be difficult. This is a result of complex lotic
macroinvertebrate communities which thrive in reaches consistent with heterogeneous
habitats. As well as downriver where there are natural but distinctly different characteristics in
headwaters, mid-reaches and lower reaches (Vannote et al. 1980). Additionally, all
communities present are altered and influenced temporally, as seasons alter habitats, flow,
temperatures, nutrients and food availability (Yazdian et al. 2014; Alvarez-Cabria et al. 2010;
Stark & Phillips 2009; Thompson & Townsend 1999), and spatially, as habitats and
conditions vary longitudinally down river (Hopkins et al. 2011; Alvarez-Cabria et al. 2010;
Vannote et al. 1980) and across reaches (Collier et al. 2013). Furthermore, biological
community differences, induced by spatial and temporal variations, indicate the considerable
influence of physio-chemical conditions (e.g. flow, temperature and nutrients) (Latha &

21. 12
Thanga 2010; Varnosfaderany et al. 2010). Expressing support for the previous assumption
that biological communities can be indicative of certain physio-chemical variables (Barbour et
al. 2000). Specifically, in regard to temporal variations, macroinvertebrate community
proportions can alternate throughout the year, although, it has been identified that annual
dominant macroinvertebrate taxa can remain the same (e.g. Ephemeroptera, Trichoptera and
Diptera) (Mathuriau et al. 2008). In relation to spatial variations, physio-chemical variables
can change considerably as a result of longitudinal land-use variations (i.e. down a
disturbance gradient). Higher disturbances cause river health reductions (Allan 2004; Paul &
Meyer 2001; Ometo et al. 2000), indicated by increases in pollution tolerant taxa populations
alongside decreases in pollution sensitive taxa.
For decades ecologists have identified that landscapes highly influence rivers (Allan 2004).
However, further evidence is still required to understand how river health (indicated by
macroinvertebrate communities) responds to varying types and intensities of land-uses.
Alongside changes to physio-chemical variables resultant from land-use changes,
geographical locations, interactions between biological communities and organism dispersal
also have profound impacts on macroinvertebrate communities (Kay et al. 2001; Boothroyd &
Stark 2000). As a consequence of numerous influential factors imposed onto
macroinvertebrate communities, thorough and specific data collection and analysis is required
for every river health investigation conducted; as no two rivers are identical.

22. 13
1.3.2 Ecosystem Metabolism
Ecosystem metabolism (hereafter referred to as ‘metabolism’) is a widely utilized river health
indicator because it is very sensitive to changes imposed by human disturbances or from
natural disturbances (Young et al. 2008). Metabolism consists of the functional processes
gross primary production (GPP) and ecosystem respiration (ER) (Young et al. 2008;
Vugteveen et al. 2006), whereby both directly measure the biological community’s diurnal
productivity (i.e. fish, aquatic plants, invertebrates, algae, microbes). As a consequence
metabolism enables estimates of food availability and the transfer of energy through a river
system (Young et al. 2008). There are numerous factors that influence and control GPP and
ER (Table 1.4), all of which can vary temporally (Clapcott & Barmuta 2010; Roberts et al.
2007; Young & Huryn 1996), spatially (Bunn et al. 2010; Clapcott et al. 2010; Clapcott &
Barmuta 2010) and as a result of external disturbances (e.g. agriculture and urbanisation)
(Figure 1.4) (Yates et al. 2013). However, although the influential factors of metabolism are
listed individually (see Table 1.4) they can interact profoundly. As a result combinations can
exacerbate the impacts imposed onto GPP and ER; some factors only affect GPP, some only
impact ER, whilst other factors can impact both (Young et al. 2008).
Both GPP and ER can be determined by measuring diel oscillations of dissolved oxygen (DO)
concentrations (for a minimum of 24 hours) with either the single open station approach
(Silva-Junior et al. 2014; Collier et al. 2013; Odum 1956) or by the closed chamber approach
(Bunn et al. 1999; Clapcott & Barmuta 2010). Although, both of these approaches measures
GPP and ER at different scales, they are considered to be highly beneficial. Firstly, the single
open station approach works at the reach scale by determining GPP and ER for the whole
length of the river reach; however it can be strongly influenced by surface re-aeration (Yates
et al. 2013; Young et al. 2008). In contrast, the closed chamber approach measures at a patch
scale focusing on specific primary producers and river bed substrata (Clapcott & Barmuta
2010; Aristegi et al. 2009). Furthermore, the components of metabolism (i.e. GPP and ER)
can be utilized to calculate the photosynthesis: respiration ratio (P:R). Previously it has been
described in the river continuum concept that a river naturally transitions spatially and
temporally between an autotrophic (P:R > 1) (carbon is internally sourced) and heterotrophic
(P:R<1) system (carbon is externally sourced) (Vannote et al. 1980). Hence, the P:R can
provide an indication of how carbon is sourced and the quantity of food available in the river
ecosystem (Young et al. 2008). Consequently, investigative results can be compared with the
RCC concept, developing further, required understanding on if and how land-uses can change

23. 14
systems in relation to expected natural transitions (e.g. Young & Collier 2009). For instance,
if a forested stream has its riparian vegetation removed then the river can transition from a
heterotrophic system to an autotrophic system.

24. 15
Table 1.4-How different factors can influence metabolism within a river ecosystem.
Factor Influence on Metabolism References
Sedimentation -Deposition of high sediment loads can
smother aquatic plant life, impeding GPP.
-In contrast respiration rates can increase.
Bunn et al. (1999)
Turbidity -Low and intermediate turbidity conditions
were identified to be a result of high GPP; as
light availability is increased.
Hopkins et al. (2011)
Light Availability -As light availability increases GPP distinctly
rises.
-Whilst a decrease in light availability
reduces GPP; this variable can be controlled
by turbidity and riparian vegetation.
Hopkins et al. (2011)
Roberts et al. (2007)
Fellows et al. (2006)
Bunn et al. (1999)
Nutrient Concentrations
(predominantly nitrogen
and phosphorous)
-ER is highly influenced by nutrients; when
enrichment transpires ER increases.
Hopkins et al. (2011)
Riparian Vegetation -The removal of riparian zones increases
GPP because light availability increases.
-Whilst ER increases as a result of nutrient
enrichment.
-In contrast, riparian vegetation presence
reduces GPP,a result of shading.
Fellows et al. (2006)
Bunn et al. (1999)
Habitat Types -Habitats primarily consisting of depositional
sediment have been identified to promote
high ER; a result of higher microbial
presence.
-In gravel sediments ER can be considerably
lower.
Clapcott & Barmuta
(2010)
Flow -As flow increases (e.g. excess of 150m3
s-1
),
particularly during flood conditions, the
biological community can experience either
catastrophic drift or scouring, resulting in
reductions in GPP and ER.
Young et al. (2008)
Uehlinger (2006)
Young & Huryn. (1996)
Temperature -Temperature can cause metabolism
oscillations.
-However,GPP and ER can both increase as
a result of temperature rises.
Clapcott & Barmuta
(2010)
Roberts at al. (2007)
Uehlinger (2006)
Scales -GPP is influenced predominantly by
catchment scale variables.
-ER is driven primarily by reach scale
variables.
Yates et al. (2013)
Clapcott & Barmuta.
(2010)
Uehlinger (2006)

25. 16
Figure 1.4- Drivers of Gross Primary Production and Ecosystem metabolism that can cascadedown to
river function at a patch scale(Yates et al.2013).

26. 17
1.3.3 Organic Matter Decomposition
Organic matter decomposition (OMD) is an important health indicator that integrates the
breakdown conducted by both microbial and macro-invertebrate activities (Clapcott &
Barmuta 2010; Young et al. 2008). OMD is a widely utilized health indicator because it is
highly sensitive to river ecosystem changes. Subsequently, it can provide evidence on if and
how human land-use activities and intensities impact river health (Young et al. 2008). There
are three main factors that control rates of organic matter decomposition, riparian vegetation
cover / composition (Vysna et al. 2014; Clapcott et al. 2010), nutrient concentrations
(Woodward et al. 2012; Hopkins et al. 2011) and temperature (Collier et al. 2013; Clapcott &
Barmuta 2010; Young et al. 2008; Uehlinger 2006). Although, pH and sediment can also be
influential factors (Young et al. 2008). Nutrient concentrations and temperatures are measured
at a reach scale, whilst riparian vegetation cover endeavours at a catchment scale. This is
essential to distinguish because riparian vegetation cover directly influences and catalyses
modifications to nutrients and temperatures and thus OMD; indicating that human impacts (in
this case riparian removal) can cascade down to lower scales (Young et al. 2009; Vugteveen
et al. 2006; Allan 2004) (Figure 1.5). For instance, an investigation conducted in the south
island, New Zealand, found that cellulose decomposition (i.e. OMD) was accelerated when
80-100% of the riparian vegetation was removed (Clapcott et al. 2010). These results
suggested that substantial rises in temperatures and nutrient concentrations were caused
(Figure 1.5). Similarly, alongside riparian vegetation removal, organic pollution (e.g. sewage
inputs) and toxic chemicals induce river ecosystem alterations, both of which are caused by
human activities. As a consequence, OMD decreases as a result of toxin chemical inputs,
whereas, OMD increases as a result of organic pollution (i.e. nutrient inputs) (Young et al.
2008).
Furthermore, it has been identified that the physio-chemical factors (i.e. temperature and
nutrients) that control OMD oscillate spatially (Young & Collier 2009; Young et al. 2008) and
temporally (Young & Huryn 1996). Natural disturbances (e.g. seasons) and consequent
physio-chemical variations are not a concern as OMD will predominantly remain within a
healthy range. However, human influences are posing a great global concern because as
activities intensify, further river health abatements will follow.
Additionally, in regard to OMD measurements, previous investigations predominantly utilized
leaf litter assays. Leaf litter can be standardised by using mesh bags, however, the resultant

27. 18
OMD can be underestimated and comparisons between studies can prove difficult if different
leaf types were used (Young et al. 2008). In response to this, cotton strip assays have recently
been proposed as an alternative method. This method is appropriate because cotton strips are
composed of cellulose and can be standardized providing comparable data (Imberger et al.
2010; Tiegs et al. 2007; Boulton & Quinn 2000).

28. 19
Riparian Vegetation
Removal
No riparian vegetation to
intercept surface runoff.
Canopy cover is removed.
Light availability
increases.
Increased sediment
loadings are added.
Nutrient concentrations
in the river increases;
Nutrients are mobilised in
surface runoff as they
adsorb to soil particles.
Internal river
temperatures increase;
more energy is available
to heat up river water.
OMD acceleration
Human
DisturbanceRiverConditionAlterations(changestophysio-chemicalfactors)Impact
Figure 1.5- Alterations of two physio-chemical factors and theoverall impact,in regard to OMD,
when riparian vegetation is removed by human activities (i.e.land uses) (modified from Vysna et al.
2014; Collier etal.2013; Woodward et al.2012;Hopkins et al.2011;Clapcottet al.2010;Clapcott
& Barmuta 2010; Young et al.2008;Uehlinger 2006).

29. 20
1.4 Maitai River and Research Objectives
The aim of this study was to determine the spatial and temporal health of the Maitai River in
relation to longitudinal variations in land-uses. The objectives of this study include
determining the summer rates of ecosystem metabolism of the river using the open-system
dissolved oxygen technique, determining organic matter decomposition with the use of cotton
strips, and investigating the macroinvertebrate communities present using quantitative
methods. The hypotheses of this investigation are as follows:
1. Ecosystem metabolism (GPP and ER) will vary spatially, with rates indicating
healthy conditions at the control site ‘South Branch’, alongside decreases in health
downstream as land-use impacts increase. Additionally, the Maitai will show a
significant transition downstream from heterotrophy to autotrophy (Vannote et al
1980).
2. Ecosystem metabolism (GPP and ER) will vary temporally and be significantly
higher in the warmer months of December and January. Increased temperatures
stimulate higher growth rates increasing GPP and stimulate microbial activity
increasing ER (Roberts at al. 2007; Uehlinger 2006).
3. Organic matter decomposition will vary spatially with rates indicative of healthy
conditions at the South Branch control site.
4. Organic matter decomposition will be significantly higher in the warmer months of
December and January because microbes and invertebrates will be stimulated in the
warmer temperatures (Collier et al. 2013; Uehlinger 2006; Young et al. 2008).
5. Macroinvertebrate communities will vary longitudinally reflecting the River
Continuum Concept. Benthic invertebrate metrics will show that the South Branch
control site will be significantly healthier compared to the rest of the sites (Avon
terrace, Dennes Hole, Campground and Site B). This is because the south Branch has
the highest habitat quality because less biological, physical and chemical stresses
from human influences have been imposed.

30. 21
2.0 Materials and Methods
2.1 Study Area and Sites
The Maitai River studied in this investigation is perennial with an annual flow of 124 m3s-1. It
flows for approximately 12km in a north easterly direction from source to sea. On route it
flows through Nelson city, south island, New Zealand and terminates in Tasman Bay
(41°16'11.7"S, 173°17'11.2"E). The 85km2 catchment has a complex morphology consisting
of the Dun mountain ophiolite belt, Brook Street volcanics and Maitai group conglomerate
(Crowe et al. 2004) all of which are west of the alpine fault (Mills 2015.Unpublished). The
Nelson area has a mild climate with 12-22oC in summer and 4-14oC in winter. The annual
rainfall is on average 1043mm, whilst, the annual sunshine is approximately ~2,449 hours
(Nelson and Tasman City Council).
River health metrics (metabolism, OMD and macroinvertebrate communities) were measured
at six 50m long river reaches located longitudinally down the Maitai River; covering
approximately ~9km (Figure 2.1; Figure 2.2; Figure 2.3). However, the ‘Golf Course’ and the
‘Campground’ sites were only approximately 400m apart with similar peripheral
environments. Therefore, the results from the Golf Course and the Campground were
combined, hereafter referred to as the ’Campground’. Land-uses varied (Table 2.1) with
higher native vegetation surrounding the upper two sites; South Branch and Site B (77%).
However, Site B does have the Maitai reservoir’s backfeed discharge pipe located directly
upstream. In contrast, the lower 3 sites (Avon Terrace, Dennes Hole, Campground) had higher
human influences in there surrounding areas (e.g. 33% of exotic vegetation around Avon
Terrace). Riparian shading was higher at the upper two sites (Site B 49% and South Branch
49%), whilst habitat types and substrate varied between all sites (Table 2.1). (Mills
2015.Unpublished)

31. 22
Site Site Description Co-ordinates Land use % Average
Wetted Area
(m)
Dominant
Substrate Sizes
(%)
Habitat Type Reach
Riparian
Shading(%)
Size
Approximation
of Riparian
Zone (m)
Native
Riparian Zone
(%)
Upstream
Native
Vegetation
Avon
Terrace
In the lower reaches of theMaitai
River the study sites consisted
‘Avon Terrace’ (the most
downstream reach), ‘Dennes
Hole’ and the ‘Maitai
campground/
Golf course’. All three (especially
Avon Terrace andDennes hole)
are visitedby the public or in
close proximity to public areas.
41°16'24.5"S
173°17'33.6"
E
Urban 3%
-Bare ground1 %
-Pasture 6%
-Native vegetation
55%
-Exoticvegetation
33%
11.4 Fine Gravel 5%
Gravel 45%
Cobble 46%
Boulder 0%
Bed rock 0%
50% Run, 50%
riffle.
22% 2m 0% 59%
Dennes Hole 41°16'20.3"S
173°18'27.0"
E
-Urban 1%
-Bare ground1%
-Pasture 6%
-Native vegetation
55%
-Exoticvegetation
36%
8.9 Fine Gravel 1%
Gravel 21%
Cobble 68%
Boulder 10%
Bed rock 0%
85% Riffle,
15% run.
12% 4-5m 8.7% 59%
Camp
ground and
Golf course
Campground:
41°17'18.6"S
173°19'32.0"
E
Golf course:
41°17'06.3"S
173°19'52.8"
E
-Urban 1%
-Bare ground1%
-Pasture 7%
-Wetland2%
-Native vegetation
72%
-Exoticvegetation
19%
9.5 Fine Gravel 1%
Gravel 20%
Cobble 69%
Boulder 11%
Bed rock 0%
90% Riffle
10% Run
30% 10-12m 5.3% 77%
Site B ~approximately600mbelowthe
backfeedinput; this is a rural site
with lowpublic visitation
especially as site accessibilityis
difficult.
41°17'50.8"S
173°22'02.4"
E
-Bare ground2%
-Pasture 15%
-Native vegetation
77%
-Exoticvegetation 6%
9 Fine Gravel 2%
Gravel 33%
Cobble 22%
Boulder 39%
Bed rock 5%
25% pool, 40%
Run, 35% riffle.
49% 6m 45% 95%
South
Branch
This was the studies control site,it
was longitudinally the most upper
site; locatedabovethe backfeed
andthe weir.
It is in a rural location,but close
to many walkingandcycling
tracks in the Maitai valley with
potential public access.
41°17'57.6"S
173°22'00.5"
E
-Bare ground2%
-Pasture 15%
-Native
vegetation77%
-Exoticvegetation 6%
10 Fine Gravel 0%
Gravel 39%
Cobble 50%
Boulder 9%
Bed rock 2%
80% run, 20%
riffle.
49% 4-5m 45% 95%
Table 2.1- Study site descriptions longitudinally alongtheMaitai River (Mills2015.Unpublished).

32. 23
Figure 2.1- All of the sites used to assess river health in the Maitai River;from the highest siteat the South Branch down to the lowest sites atAvon Terrace (Land
Information New Zealand Data Service).
Maitai River Sites

33. 24
Figure 2.2- Three of the sites used to investigate river health, 1 indicates a downstreamviewpoint whilst2
shows a upstream view pointat each site; (A) shows the South Branch,(B) shows Site B and (C)is at the
Campground site.
A1 A2
B1 B2
C1 C2

34. 25
D1 D2
E1 E2
F1 F2
Figure 2.3- Three of the sites used to investigate river health, 1 indicates a downstreamviewpoint whilst2
shows a upstream view pointat each site; (D) shows the Golf course, (E) shows Dennes Hole and (F ) is at
Avon Terrace.

35. 26
2.2 Periphyton Cover
Periphyton cover was assessed weekly at the lower three sites (Avon Terrace, Dennes Hole
and Campground), and monthly at the upper two sites (Site B and South Branch). A
periphyton viewer was used to estimate the percentages of periphyton cover at five points
along five transects across each river reach section being investigated (Figure 2.4), based on
modified protocols of Biggs & Kilroy (2000) and Parkyn et al. (2010).
Fine sediment=20%
Green algae=15%
Diatoms=65%
Cyanobacteria=0%
Figure 2.4- A Periphyton viewer being used in the field to estimate fine sediment, green
algae, diatoms and cyanobacterial cover.

36. 27
2.3 Environmental Variables
2.3.1 Nutrient Concentrations
At weekly and monthly intervals, a 250ml water sample was collected from each site for
subsequent analysis. In the laboratory, a 45ml sub-sample was filtered and sent to Hill
Laboratories (Hamilton) for determination of nitrogen (DIN) and Nitate+N + Nitrite-N
(NNN).
2.3.2 Physiochemical Variables
At weekly and monthly intervals, stream temperature (temp), conductivity (cond), pH and
dissolved oxygen (DO) were recorded in the thalweg using a YSI WQS 650/600R.Turbidity
(turb) was measured at weekly (lower 3 sites) and monthly (upper 2 sites) intervals with a
turbidity meter; three small glass vials were filled (to achieve a mean) in the thalweg. These
samples were taken first before any of the other field work was conducted to reduce the risk
of disturbing the sediment (Figure 2.5).
Figure 2.5–The use of a turbidity meter to measure turbidity.

37. 28
2.4 Macroinvertebrates
Six Surber samples (Area=0.1m2) were collected along a 50m reach at five sites
longitudinally down the Maitai River (Avon Terrace, Dennes Hole, Campground, Site B and
South Branch) using the modified protocol C3 in Stark et al. (2001). At each site the river was
split into 6 lanes to enable whole river representation, then by using a random number table
the sites were randomly chosen in each lane (for an example see Appendix 1; Table 7.1).
Once samples were collected they were stored in 70% ethanol until identification. Samples
were identified to the lowest possible taxonomic level (Figure 2.6) (Winterbourn et al. 2006).
However, as a result of time constraints only four out of six samples from each site were
identified.
Figure 2.6- Part of the macroinvertebrate identification process ,(A)
depicts the sieves used to separate the organic matter from the
invertebrates, whilst, (B and C) showtwo samples of macroinvertebrates
in petri dishes ready for microscopic analysis.

38. 29
2.5 Cotton Strip Assay
Unbleached cotton strips (EMPA, St Gallen, Switzerland) were used to measure the rate of
organic matter decomposition potential (OMD) at five sites using a slightly modified method
from Parkyn et al. (2010) from December 2014-March 2015 (Figure 2.7). Each strip (26cm
long by 4 cm wide) was attached with nylon string to a warratah deployed in a riffle habitat.
Ten cotton strips were deployed per site.
A pilot study identified that the initially advised 7 and 14 day deployment times (Young &
Collier 2009; Clapcott et al. 2012) were not sufficient in the Maitai to achieve at least 50%
breakdown. Therefore, cotton strips were removed at approximately monthly intervals (22-31
days). After retrieval the string was removed, the cotton was gently cleaned to remove organic
matter and frozen flat until further processing. When the strips were ready for processing they
were gently washed after thawing and then oven dried at 40oC for 24 hours.
Tensile strength (Kgf) was measured using a tensometer (Sundoo Instruments, Wenshou,
China) and cellulose decomposition potential calculated relative to a control. A range in strip
widths (10-100 threads) were used as controls for when treatment strips were decomposed to
less than 100 threads wide. Temperature loggers were also deployed at the same sites to
enable temperature differences to be compensated for in the spatial investigation.
A B
~30 day
deployment
Figure 2.7– Cotton strips used to measure OMD; before deployment (A), and after approximately ~30 days of
deployment (B).

39. 30
2.6 Metabolism
Diurnal dissolved oxygen and temperature oscillations were measured at five sites using
optical D-Opto loggers (Zebra-tech, Nelson, New Zealand). Measurements were collected at
15 minute intervals from October 2014- February 2015. Average reach depth 500m upstream
of each site was also measured in March and April at the 5 sites.
Metabolism (GPP and ER) was calculated using a spreadsheet model described in Young &
Collier (2009). In summary, mean daily ecosystem respiration (ER) and the reaeration
coefficient (k) were calculated using the night-time regression method (Owens 1974). The
resultant reaeration coefficient and ER rate were then utilized to determine gross
photosynthetic rate over the sampling period; the following equation was used:
GPPt = dO2 / dt + ER - kD
Where GPPt is the gross photosynthetic rate (g O2 m-3 s-1) over the time period (t).
Coefficients of determination for the night-time regression method resulted in mean R2 values
of 0.87 (±0.10 SD), providing confidence in the method to estimate reaeration and thus
calculate metabolic variables in this investigation. Daily gross primary production (GPP) was
estimated as it was essential out of the temperature-corrected photosynthetic rates during
daylight (Wiley, Osborne & Larimore, 1990). Areal estimates were also calculated by
multiplying the volume-based estimates by mean reach depth (m), this enabled site
comparisons as depth variations at each site were accounted for.

40. 31
2.7 Data Analysis
To analyse health indicator responses (metabolism, OMD and macroinvertebrates) the
statistical programme IBM SPSS (2010) was utilized. Initially, normality was evaluated for
all the variables; once normality was met a one-way ANOVA test was conducted. If a spatial
or temporal significant difference was obtained then further analysis was performed by using
a Post Hoc Tukey Test. To test for potential causal factors associated with each indicator, a
Pearson’s Correlation test was conducted, whereby the indicators were tested against
physiochemical and biological variables (i.e. substrate, total periphyton cover, cyanobacterial
cover, green algae cover , diatom cover, fine sediment cover, DO, conductivity, turb, temp,
pH, DIN, NNN and flow). However, it must be noted that for metabolism and OMD the larger
and thus more reliable dataset was obtained from the lower three sites because of more
frequent weekly sample collections. Whereas, datasets obtained from the upper two sites were
considerably smaller and hence less reliable and representative; particularly as there were
monthly data losses resultant from storm events. Additionally, in regard to the
macroinvertebrate communities only a spatial analysis could be conducted. This is because
the investigation was carried out over summer months, whereby, significant
macroinvertebrate temporal variations would only be present between seasons.
2.7.1 Metabolism
The rates of ER (O2/m2/day) and GPP (O2/m2/day) were used in this investigation to identify
the Maitai River’s metabolism. GPP and ER were log10 transformed for analysis; GPP was
positively skewed so a simple log10 transformation was conducted, but ER was negatively
skewed, therefore, it had to be reflected and then log10 transformed. Additionally, the
Photosynthesis:Respiration ratio (P:R) was calculated by simply dividing photosynthesis with
respiration. If the result was <1 then a heterotrophic system was determined, if the result was
>1 then an autotrophic system was identified.

41. 32
2.7.2 Organic Matter Decomposition
Both % cotton tensile strength loss (%CTSL) and k coefficients (kd-1 and kdd-1) were
calculated to assess OMD. The k coefficient which expressed logarithmic exponential loss
was used to enable literature comparisons. kd-1 was calculated by dividing the k coefficient by
cotton deployment days to remove the influence of varying deployment timeframes.
Temperature was still a driver of kd-1, therefore, kd-1 was used in the temporal investigation.
Whereas, for the spatial investigation to test significance between sites the variable kdd-1 was
used. This was calculated by dividing the k coefficient by temperature and deployment days;
by accounting for site variations it allows site comparisons. During statistical analysis both kd-
1 and kdd-1 did not meet the requirements of parametric tests, therefore they were both log10
transformed. Following this one-way ANOVA’s and Post Hoc tests were performed on the
dataset (n=320). However, %CTSL was the main OMD response variable used within this
investigation. For the spatial investigation the %CTSL dday-1 (calculated by dividing %CTSL
by cotton deployment days and temperature) values met the normality requirements to
conduct a one-way ANOVA and Post Hoc test. Whereas, the %CTSL day-1 (calculated by
dividing %CTSL by cotton deployment days) values for the temporal investigation were not
normally distributed, hence the values had to be log10 transformed.
2.7.3 Macroinvertebrate Communities
Macroinvertebrate health matrices were calculated (MCI, QMCI, EPT taxa and Density) using
an excel computer model, developed by the author of Stark (1993) and Stark (1998).
Additionally, a spatial analysis was carried out to determine if the macro-invertebrate
communities varied between sites. Initially, it was determined that the MCI Scores, QMCI
Scores and EPTTaxa values were of normal distributions meeting the parametric test
requirements. Whereas, density did not meet the normality requirements, hence a log10
transformation was calculated, allowing all datasets to be analysed with one-way ANOVA
tests and Post Hoc tests.

42. 33
3.0 Results
3.1 Metabolism
Mean GPP and ER varied between sites, the highest values occurring at Avon Terrace, whilst
the lowest GPP’s were at the South Branch alongside the lowest ER’s at the Campground
(Table 3.1). However, it is important to note that Avon Terrace, Dennes Hole and the
Campground were sampled weekly providing 30 measurements, in contrast to the upper sites
(Site B and the South Branch) which were sampled monthly (n=4).
Table 3.1-The ranges and averages of GPP and ER at all the five sites along the Maitai River.
Site MeanGPP (g O2m2day-2) MeanER (g O2m2day-2)
Avon Terrace 6.46 (1.44-18.31) (n=10) 4.22 (1.56-9.56) (n=10)
Dennes Hole 2.80 (1.12-6.02) (n=10) 2.18 (0.79-3.59) (n=10)
The Campground 1.16 (0.47-2.57) (n=10) 0.62 (0.02-1.49) (n=10)
Site B 0.72 (0.68-0.77) (n=2) 0.72 (0.46-0.97) (n=2)
South Branch 0.44 (0.25-0.62) (n=2) 0.71 (0.68-0.75) (n=2)
3.1.1 Metabolism Spatial Patterns
The P:R ratio at the five sites in the Maitai River transitioned between heterotrophic and
autotrophic systems in a downstream direction (Figure 3.1 - Figure 3.2). Specifically the
upper two sites (South Branch and Site B) were predominantly heterotrophic systems from
Nov 2014-Feb 2015 (Figure 3.1; A & B). Whereas, in contrast the lower three sites (Figure
3.2; C, D & E) were found to be predominantly autotrophic systems from Oct 2014- Feb
2015.

43. 34
Figure 3.1- The transition between an autotrophic and a heterotrophic system; (A) South Branch,
(B) Site B, from Nov 2014-Feb 2015.
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
P:R
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
P:R
A
B
Autotrophic
Autotrophic
Heterotrophic
Heterotrophic
Date

44. 35
0.10
1.00
10.00
P:R
Heterotrophic
Autotrophic
0
0.5
1
1.5
2
2.5
3
3.5
4
10/16/2014 11/16/2014 12/16/2014 1/16/2015 2/16/2015
P:R
Date
Autotrophic
Heterotrophic
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
P:R
Heterotrophic
Autotrophic
C
D
E
Figure 3.2- The transition between an autotrophic and a heterotrophic system; (C) campground,
(D) Dennes Hole, (E) Avon Terrace from Oct 2014-Feb 2015.

45. 36
Initially, the Avon Terrace, Dennes Hole and Campground dataset was used for the spatial
investigation because a larger dataset had been acquired from weekly sampling (n=30). GPP
and ER were significantly different between sites (one-way ANOVA; GPP, F2,27=11.918,
P<0.01 and ER F2,27=17.160, P<0.01). Furthermore, additional analysis (Post Hoc Test)
indicated that both GPP and ER were significantly lower at the Campground compared to
Dennes Hole (P <0.05) and Avon Terrace (P <0.01) (Figure 3.3).
Figure 3.3- A Comparison between the lower 3 sites for GPP and ER; Avon Terrace=1,
Dennes Hole=2, Campground=3.

46. 37
Secondly, the spatial investigation was expanded to all five sites. But, it must be noted that
this dataset was considerably smaller because sampling at the two upper sites (South Branch
and Site B) was only monthly and because of missing data (n=10). GPP had a significant
difference between sites (one way ANOVA, F4,5=7.255, P=0.026), unlike ER which was
found to have no significant difference. As a result analysis was extended to investigate which
sites had specific differences for GPP (post hoc test). GPP was significantly lower at the
South Branch compared to Avon Terrace (Figure 3.4).
In contrast, there was no significant difference over time in regard to ER (F9,20= 0.342,
P>0.05) and GPP (F9,20=0.497, P>0.05). As a result a post hoc test was not required.
Figure 3.4- A Comparison of GPP for all five sites; 1.0 Avon Terrace, 2.0 Dennes Hole,
3.0 The Campground, 4.0 Site B and 5.0 the South Branch.

47. 38
3.1.2 Relationships Between Metabolism, Physicochemical and Biological Variables
The Avon Terrace, Dennes Hole and Campground dataset was used for relationship testing as
the data sizes for the south Branch and Site B were of an inadequate size to test for
correlations. GPP and ER were significantly correlated (positive) (P<0.01). Additionally, GPP
and ER were both significantly (positive) correlated with dissolved inorganic nitrogen (DIN)
(P<0.05), however, GPP was also significantly correlated (negatively) with cyanobacterial
cover (cyano) (P<0.05). ER was significantly correlated (negatively) with turbidity (P<0.05),
conductivity (P<0.05), green algae cover (Greens) (P<0.05) and pH. (P<0.05) (Table 3.2).
Table 3.2-The correlations of GPP and ER with biological and physicochemical variables.
Pearson correlations (P<0.05) (n=30)
Correlated Variables Correlation values
GPP ER 0.629**
DIN 0.412*
Cyano -0.446*
ER GPP 0.629**
Turbidity -0.389*
Conductivity -0.420*
DIN 0.389*
pH. -0.396*
Greens -0.377*
** Correlation significant at the 0.01 level (2-tailed).
* Correlation significant at the 0.05 level (2-tailed).

48. 39
3.1.3 Metabolism River Health Results
Metabolism results were assessed in relation to the ecosystem health values for good,
satisfactory and poor proposed by Young et al. (2008). Firstly, in regard to the health values
for GPP and ER, it was found that the lower 3 sites (Avon Terrace, Dennes hole and the
Campground) had numerous variations in health spatially and temporally. However, a high
proportion of these values represent either healthy or satisfactory river health’s (Parkyn et al.
2010) (Figure 3.6). In contrast, the South branch and Site B expressed considerable variations,
with healthy and poor results present at the same sites (Table 3.3).
The South Branch and Site B expressed relatively healthy levels from November 2014-
February 2015 (Figure 3.7). In contrast, Avon Terrace, Dennes Hole and the Campground
showed a transition between poor, satisfactory and healthy values from October 2014 to
February 2015 (Figures 3.8 and 3.9). Flow appears to be a contributing factor to this
variation, as when flow increased the P:R ratio had risen (Figure 3.5, Table 3.4).
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0
0.5
1
1.5
2
2.5
3
3.5
4
10/16/2014 11/16/2014 12/16/2014 1/16/2015 2/16/2015
Flowm3s-1
P:RRatioValues
Date P/R Flow
Poor
Satisfactory
Healthy
Figure 3.5- The relationship between the P:R and flow at Avon Terrace in relation to the P:R
ratio health boundaries (Avon Terrace was the only site with accurate and accessible flow data)
(Parkyn et al. 2010).

49. 40
0
2
4
6
8
10
12
10/8/2014 10/28/2014 11/17/2014 12/7/2014 12/27/2014 1/16/2015 2/5/2015 2/25/2015 3/17/2015
ER(O2/m2/day)
Date Avon Terrace Dennes Hole Campground
Poor
A
Satisfactory
Satisfactory
Healthy
Poor
0
2
4
6
8
10
12
14
16
18
20
10/8/2014 10/28/2014 11/17/2014 12/7/2014 12/27/2014 1/16/2015 2/5/2015 2/25/2015 3/17/2015
GPP(O2/m2/day)
Date Avon Terrace Dennes Hole Campground
B
Poor
Satisfactory
Healthy
Figure 3.6- The temporal and spatial variation in health values for the lower 3 site; (A) Ecosystem
Respiration and (B) gross primary production (Parkyn et al. 2010).

50. 41
Table 3.3-The health values found at Site B and The South Branch from Nov 2014 and Feb 2015.
Site Metabolism component Health
GPP
(gO2/m2/day)
ER
(gO2/m2/day)
Site B
22/11/14
0.68 0.46 GPP= Healthy
ER=Poor
Site B
17/02/15
0.77 0.97 GPP= Healthy
ER=Satisfactory
South Branch
22/11/14
0.62 0.75 GPP= Healthy
ER= Satisfactory
South Branch
17/02/15
0.25 0.68 GPP= Healthy
ER= Poor
Table 3.4- The health boundaries for P:R, GPP and ER (Parkyn et al. 2010).
Health Category P:R GPP ER
Healthy <1.3 <4.0 1.5-5.5
Satisfactory 1.3-2.5 4.0-8.0 0.7-1.5 or 5.5-10.0
Poor >2.5 >8.0 <0.7 or >10.0

51. 42
Healthy
Satisfactory
B
16/10/14 16/11/14 16/12/14 16/01/15 16/02/15
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40P:R
A
Satisfactory
Healthy
16/10/14 16/11/14 16/12/14 16/01/15 16/02/15
Figure 3.7- Health transitions from Oct 2014-Feb 2015 at sites (A) South Branch and (B) Site B
(Parkyn et al. 2010).

52. 43
0
1
10
P:R
C
Poor
Healthy
Satisfactory
16/10/14 16/11/14 16/12/14 16/01/15 16/02/15
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
P:R
D
16/10/14 16/11/14 16/12/14 16/01/15 16/02/15
Poor
Satisfactory
Healthy
Figure 3.8- Health transitions from Oct 2014-Feb 2015 at sites (C) Campground and (D) Dennes Hole
(Parkyn et al. 2010).

53. 44
0
0.5
1
1.5
2
2.5
3
3.5
4
10/16/2014 11/16/2014 12/16/2014 1/16/2015 2/16/2015
P:R
Date
E
16/10/14 16/11/14 16/12/14 16/01/15 16/02/15
Figure 3.9- Health transitions from Oct 2014-Feb 2015 at Avon Terrace (Parkyn et al. 2010).
0
Poor
Satisfactory
Healthy

54. 45
3.2 Cotton Strip Assays
Percent cotton tensile strength loss (%CTSL), kd-1 and kdd-1(Table 3.5) varied spatially and
temporally, with indications that there were significant differences between sites. Avon
terrace had the highest %CTSL on the 10/02/2015, whilst the highest %CTSL for the South
Branch was exhibited on the 13/01/2015 (Table 3.5).
Table 3.5- The ranges for the k-coefficients kd-1 (divided by deployment days),kdd-1 (divided by deployment
days and temperature) and %CTSL for each cotton collection date at each site from the Maitai River.
Site Date Kd-1
Kdd-1
%CTSL
Avon terrace 22/12/2014 0.021-0.098 0.0012-0.0056 47.59-95.26
13/01/2015 0.011-0.079 0.0008-0.0041 21.68-82.60
10/02/2015 0.005-0.066 0.0003-0.0032 13.31-84.19
11/03/2015 0.012-0.081 0.0006-0.0042 30.01-90.40
Dennes Hole 22/12/2014 0.0048-0.0282 0.00031-0.00180 13.81-58.22
13/01/2015 0.0050-0.0272 0.00025-0.00139 10.37-45.02
10/02/2015 0.0053-0.0290 0.00027-0.00145 13.87-55.54
11/03/2015 0.0036-0.0301 0.00019-0.00159 9.84-58.26
The Campground 22/12/2014 0.0027-0.0346 0.00016-0.00208 8.13-65.83
13/01/2015 0.0016-0.0319 0.00009-0.00169 13.31-50.42
10/02/2015 0.0040-0.0192 0.00020-0.00098 10.49-41.65
11/03/2015 0.0013-0.0117 0.00007-0.00063 3.70-28.89
Site B 22/12/2014 0.0044-0.0206 0.00031-0.00144 12.68-47.16
13/01/2015 0.0098-0.0593 0.00064-0.00390 19.34-72.85
10/02/2015 0.0029-0.0371 0.00017-0.00223 7.70-64.63
11/03/2015 0.0018-0.0141 0.00011-0.00090 6.34-34.48
South Branch 22/12/2014 0.0046-0.0174 0.00035-0.00143 12.40-41.64
13/01/2015 0.0031-0.0356 0.00020-0.00225 6.60-54.33
10/02/2015 0.0049-0.0192 0.00038-0.00148 12.81-41.54
11/03/2015 0.0014-0.0194 0.00010-0.00133 4.23-44.18

55. 46
3.2.1 Cotton Strip Assay Spatial Patterns
Percent CTSL dday-1 was significantly different between the five sites ( F(4,15)= 5.149,
P<0.01). But, after following extended analysis (Post Hoc Test) it was found that the initial
significant difference was specifically as a result of %CTSL dday-1 being significantly higher
at Avon Terrace (P<0.05) compared to the other four investigated sites (Figure 3.10); Dennes
Hole, Campground, Site B and the South Branch.
Figure 3.10- The %CTSL per dday at each of the five sites; (1) Avon Terrace, (2) Dennes Hole, (3)
Campground, (4) Site B, (5) South Branch.

56. 47
3.2.2 Cotton Strip Assay Temporal Patterns
In regard to the temporal investigation %CTSL day-1 log10 exhibited no significant difference
from December 2014-March 2015 (one-way ANOVA). However, after further statistical
exploration and analysis (one-way ANOVA and Post Hoc Test), it was identified, with the use
of kd-1 log10 , that there was a significant temporal difference (F3,311=11.878, P<0.01).
Furthermore, it was specifically found that OMD was significantly higher in January (P<0.01)
compared with the rest of the months (December, February and March) (Post Hoc Tukey
Test). This can be linked to the extreme flash flood event that occurred on the 1st January
2015 (Figure 3.11).
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0
10
20
30
40
50
60
70
80
1/1/1900 1/2/1900 1/3/1900 1/4/1900 1/5/1900 1/6/1900
Flow(m3sec-1)
Cottontensilestrengthloss(%)
%ctsl
average
flow m3/sec
Figure 3.11- Relationship between flow and organic matter decomposition where high
flood events in the Maitai River may increase organic matter decomposition rates;
accurate flow data was only accessible for Avon Terrace.
01/11/14 01/12/14 01/01/15 01/02/15 01/03/15 01/04/15

57. 48
3.2.3 Relationships Between Cotton Strip Assay, Physicochemical and Biological Variables
Although numerous physicochemical and biological variables were analysed, with both the
spatial (all five sites) and temporal (four specific dates) datasets, significant positive
correlations were only found between OMD (%CTSL day-1), temperature and fine gravel
(Table 3.6). Additionally, following extended analysis the k coefficients also expressed
significant positive correlations with temperature (Table 3.7). However, interestingly it was
found that there were no significant correlations between OMD and flow.
Table 3.6- The relationships between OMD (CTSL), biological and physicochemical variables.
Table 3.7- The correlations between OMD (kd-1 and kdd-1),biological and physicochemical variables.
Pearson correlations (n=20)
Correlated Variables Significance
kd-1 mean (Spatial) Temperature P<0.01 (Positive Correlation)
kdd-1 mean (Temporal) Temperature P<0.05 (Positive Correlation)
3.2.4 Cotton Strip Assay Health Results
Cotton strip assay results were assessed in relation to the ecosystem health values for good,
satisfactory and poor proposed in Parkyn et al. (2010). The OMD values all expressed a
satisfactory health at each site and over the whole duration of the OMD investigation
(22/12/2014 to 11/03/2015).
Pearson Correlations (n=20)
Investigation Correlated Variables Significance
Spatial CTSL dday-1 Temperature Log10 P<0.05 (Positive Correlation) (n=20)
Site Number P<0.01 (Negative Correlation) (n=20)
Fine Gravel P<0.05 (Positive Correlation) (n=5)
Temporal CTSL day-1
Log10 Temperature Log10 P<0.01 (Positive Correlation) (n=20)

58. 49
3.3 Macroinvertebrate Community Compositions
Numerous different macroinvertebrate orders, families and species were identified in the
Maitai River (Appendix 2; Table 7.2). In regard to macroinvertebrate community index scores
(MCI) set out by Stark (1998) and Stark (1993), the majority of the pollution sensitive taxa
(Ephemeroptera, Plecoptera and Trichoptera taxa) were found at the upper two sites (Site B
and the South Branch, high MCI scores) (Figure 3.12), whilst the pollution tolerant taxa (low
MCI scores) dominated the lower 3 sites; particularly at Avon Terrace and the Campground
(Figure 3.3.1). For example 1003 Ostracoda (class) individuals and 69 Orthocladiinae
(subfamily) individuals were identified at Avon Terrace, both highly pollution tolerant. In
contrast, from the Plecoptera order the South Branch supported 60% of the highly pollution
sensitive Gripopterygidae, Zelandoperla spp. and 100% of the Eustheniidae, Stenoperla spp.
Identified (however, this was just one individual). Additionally, various functional feeding
groups were identified, with all the sites having between 45-60% of collector/browsers, 28-
40% of predators and 4-10% of filterers. However, the investigations control site (the South
Branch) had two functional feeding group differences; shredder presence and grazer absence
(Figure 3.13).
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Avon Terrace Dennes Hole Campground Site B South Branch
NumberofIndividuals
Sites
other
Zelandoperla sp. (P)
Stenoperla sp. (P)
Pycnocentrodes sp. (T)
Psilochorema (T)
Plectrocnemia
maclachlani (T)
Olinga feredayi (T)
Neurochorema sp.(T)
Hydrochorema spp. (T)
Hydrobiosis (T)
Confluens (T)
Aoteapsyche spp. (T)
Zephlebia (E)
Nesameletus sp. (E)
Deleatidium spp. (E)
Figure 3.12- The number of individuals of pollution sensitive
macroinvertebrate species and other species (generally pollution tolerant)
found at Avon Terrace, Dennes Hole, The Campground, Site B and The South
Branch in the Maitai River (E=Ephemeroptera, P=Plecoptera, T=Trichoptera).

59. 50
54%
7%
31%
8%
45%
10%
40%
5%
52%
9%
35%
4%
53%
7%
36%
4%
Figure 3.13- The proportion of different functional feeding groups at each site, from the Maitai River on the 3rd
and 4th of March 2015; (A) Avon Terrace, (B) Dennes Hole, (C) Campground, (D) Site B and (E) the South
Branch (Winterbourn 2000; Jaarsma et al. 1998; Winterbourn 1996; Lester et. al. 1994; Carver et al. 1991;
Colless & McAlpine 1991; Greenslade 1991; Quinn & Hickey 1990; Chadderton 1988; Winterbourn et al. 1984;
Winterbourn & Mason 1983; Cowie 1980; Winterbourn 1980; Cowley 1978).
60%
4%
28%
8%
Collectors/Browsers
Filterers
Predators
Grazers
Shredders
Key
A B
C D
E

60. 51
3.3.1 Macroinvertebrate Indices
Initial analysis of the MCI and QMCI indices identified a consistent increase in mean values
from the lowest value at Avon Terrace to the highest values achieved at the South branch.
Additionally, the highest EPTtaxa mean was identified at the South Branch. In contrast, density
was found to be irregular, exhibiting no obvious patterns (Table 3.8).
Table 3.8- The macroinvertebrate indices of MCI, QMCI, EPTtaxa and Density calculated for Avon Terrace,
Dennes Hole, The Campground, Site B and The South Branch, including each replicate value and mean value.
Site Replicate
Value
MCI QMCI EPTtaxa Density MCI
mean
QMCI
mean
EPTtaxa
mean
Density
mean
Avon Terrace 1 87.27 2.38 27.27 1280 76.02 2.91 14.36 4867.50
Avon Terrace 2 62.22 2.14 11.11 720
Avon Terrace 3 83.81 2.84 19.05 3720
Avon Terrace 4 70.77 4.30 0.00 13750
Dennes Hole 1 84.62 2.30 30.77 1170 86.57 3.44 33.88 602.50
Dennes Hole 2 82.86 4.10 42.86 200
Dennes Hole 3 97.14 3.80 28.57 660
Dennes Hole 4 81.67 3.55 33.33 380
Campground 1 85.33 1.34 26.67 11770 78.08 2.06 26.67 4160.00
Campground 2 95.00 3.15 33.33 1480
Campground 3 50.00 1.27 16.67 1540
Campground 4 82.00 2.48 30.00 1850
Site B 1 74.29 2.78 28.57 1110 81.44 3.30 24.48 887.50
Site B 2 96.25 4.00 31.25 1490
Site B 3 78.57 2.51 21.43 850
Site B 4 76.67 3.90 16.67 100
South Branch 1 110.77 5.37 30.77 350 112.69 5.81 38.53 612.50
South Branch 2 106.67 6.33 33.33 730
South Branch 3 113.34 5.03 40.00 1180
South Branch 4 120.00 6.53 50.00 190

61. 52
3.3.2 MCI Scores
MCI scores, were significantly different spatially between the sites (F4,15=6.376,
P=<0.01)(one-way ANOVA). But, after a post hoc test it was found that that the previous site
differences from the one-way ANOVA was caused by the MCI scores being significantly
higher at the South Branch compared to the rest of the sites (Figure 3.14). The rest of the sites
showed no significant differences.
Figure 3.14- The MCI scores at five sites investigated from the Maitai River (with 25th
interquartile range).

62. 53
3.3.3 QMCI Scores
QMCI scores were significantly different between the five sites (F4,15= 11.13, P<0.01)(one-
way ANOVA). Yet, further investigation found that it was only the QMCI Scores for the
South Branch site that were significantly higher than the other four sites; Avon Terrace,
Dennes Hole, Campground and Site B (P=<0.01), (Post Hoc Test) (Figure 3.15).
Figure 3.15- The QMCI scores at five sites investigated from the Maitai River (with 25th
interquartile range).

63. 54
3.3.4 Percentage EPTTaxa (Ephemeroptera, Plecoptera, Trichoptera Taxa)
The EPTTaxa results indicated that there was a significant difference between the five sites
(F4,15=5.004, P<0.01)(one-way ANOVA). But, after further analysis (Post Hoc Test) it was
specifically identified that the South Branch had significantly higher proportions of EPTTaxa
compared to Avon Terrace (P<0.01), whilst Avon Terrace had significantly lower proportions
of EPTTaxa compared to Dennes Hole (P<0.05) (Figure 3.16). The rest of the sites showed no
significant differences.
Figure 3.16- The percentage of EPTTaxa at five sites investigated from the Maitai River (with 25th
interquartile range).

64. 55
3.3.5 Density (no.2/m)
The results of the macroinvertebrate densities expressed that there was no significant
difference between all five sites in the Maitai River (F4,15=2.897, P=0.058) (Figure 3.17).
Figure 3.17- The densities of macroinvertebrates at five sites from the Maitai River (with 25th
interquartile range).

65. 56
0
20
40
60
80
100
120
140
160
Avon Terrace Dennes Hole Campground Site B South Branch
NumberofIndiviudals
Sites
Zelandoperla sp. (P) Stenoperla sp. (P) Pycnocentrodes sp. (T)
Psilochorema (T) Plectrocnemia maclachlani (T) Olinga feredayi (T)
Neurochorema sp.(T) Hydrochorema spp. (T) Hydrobiosis (T)
Confluens (T) Aoteapsyche spp. (T) Zephlebia (E)
Nesameletus sp. (E) Deleatidium spp. (E) Austroclima sp. (E)
3.3.6 Macroinvertebrate Health Results
The two upper sites (particularly the South Branch) supported a significantly higher number
of pollution sensitive individuals and species (EPTtaxa) compared to the lower three sites.
Specifically, in regard to river heath the South Branch was indicated to have the highest
health value, as it had the highest amount of pollution sensitive taxa individuals and the
highest amount of different pollution sensitive taxa species (Figure 3.18). Additionally, the
MCI and QMCI scores also indicated that the South Branch had the highest river health
(Figure 3.19, Table 3.9).
Figure 3.18- The different numbers of pollution sensitive taxa individuals and species at each site in
the Maitai River; E=Ephemeroptera, P=Plecoptera, T=Trichoptera (excluding Oxyethira albiceps, a
pollution tolerant Trichoptera) .

66. 57
Table 3.9- Health boundaries in regard to MCI Scores and QMCI score values.
Water Quality MCI score QMCI score
Excellent >120 >6.00
Good 100-119 5.00-5.99
Fair 80-99 4.00-4.99
Poor <80 <4.00
0
20
40
60
80
100
120
140
Avon Terrace Dennes Hole Campground Site B South Br control
MCIScore
Site
0
1
2
3
4
5
6
7
Avon Terrace Dennes Hole Campground Site B South Br control
QMCIScore
Site
A
B
Excellent
Excellent
Good
Good
Fair
Fair
Poor
Poor
Figure 3.19- The health of each site in regard to macroinvertebrate community (A) MCI scores
and (B) QMCI scores.

67. 58
3.3.7 Relationships Between Macroinvertebrate Communities and Physicochemical Variables
All four macroinvertebrate health indices (MCI, QMCI, EPTtaxa and Density) indicated
correlations with different physiochemical variables (Figure 3.20). DIN had a strong negative
correlation with EPTtaxa (Figure 3.20 A), whilst the MCI and QMCI scores both had a strong
negative correlation with fine sediment concentrations; however, these results only reflect
four sites instead of five as an outlier from the south branch had to be removed (Figure 3.20
B&C). Additionally, temperature, pH. , DO and conductivity had weak correlations in relation
to the MCI and QMCI scores. In contrast, Density had the strongest positive correlation in
relation to DIN and temperature (Figure 3.20 D). However, although temperature and DIN
exhibited the strongest relationships with density, these correlations were still considered to
be very weak.
R² = 0.8664
0
5
10
15
20
25
30
0 20 40 60
Dissolvedinorganicnitrogen
(mg/m3)
EPTtaxa
A
R² = 0.5608
0
1
2
3
4
5
6
7
8
9
10
75 80 85 90
Finesediment(%)
MCI values
B
R² = 0.983
0
1
2
3
4
5
6
7
8
9
10
0 2 4
Finesediment(%)
QMCI Values
C
R² = 0.3985
0
5
10
15
20
25
0 2000 4000 6000
Temperature(oC)
Density Average
D
Figure 3.20- The strongest correlations between dissolved inorganic nitrogen, fine
sediment and temperature with specific macroinvertebrate health indices; (A)
Ephemeroptera, Plecoptera and trichoptera taxa and dissolved inorganic nitrogen,
(B) MCI and fine sediment, (C) QMCI and fine sediment and (D) Density and
temperature.

68. 59
3.4 The Disturbance Gradient
The dominant land-use for all five sites was native vegetation. However, the proportions of
human disturbances (i.e. exotic vegetation, pasture, urban and bare ground) were distinctly
different between sites; particularly between the upper 2 sites (Site B and the South Branch)
and the lower 3 sites (Avon Terrace, Dennes Hole, Campground) (Figure 3.21). But, after
further analysis (regarding the river health indicators) %EPTtaxa was found to have a strong
negative correlation with human disturbance (Figure 3.22, A). In contrast, GPP, ER (Figure
3.22, B), and OMD (Figure 3.22, C) all had strong positive correlations with human
disturbance.
3% 1%
6%
56%
34%
1% 1%
6%
56%
36%
1% 1%
7%
70%
19%
2%
2%
15%
77%
6%
2%
15%
77%
6%
Urban
Bare ground
Pasture
Native vegetation
Exotic vegetation
Wetland
A B
C D
E
Figure 3.21- Land uses that surround each site; (A) Avon Terrace, (B) Dennes Hole,
(C) Campground, (D) Site B, (E) South Branch.

69. 60
Figure 3.22- The relationships between river health indicators and the proportion of
human disturbance (pasture,urban and exotic vegetation); (A) %EPT taxa,
(B) GPP (blue) and ER (red) and (C) OMD.
R² = 0.845
0
20
40
60
80
100
120
140
160
20 25 30 35 40 45
SumTotalEphermerotera,plecoptera
andTrichoptera
% Human Disturbance (land use)
R² = 0.7269
R² = 0.7582
0
1
2
3
4
5
6
7
20 25 30 35 40 45
MeanGPPandER(gO2m2day-2)
% Human Disturbance (land use)
R² = 0.5347
0
0.5
1
1.5
2
2.5
20 25 30 35 40 45
CTSLday-1
% Human Disturbance (land use)
A
B
C

70. 61
4.0 Discussion
4.1 Metabolism
The investigation identified distinct spatial variations in regard to GPP and ER and the P:R
ratio down a disturbance gradient along the Maitai River. Firstly, the P:R ratio showed a
transition from heterotrophic upper reaches down to autotrophic lower reaches, providing
support for part of hypothesis 1 and evidence that the RCC can be used in realistic river health
investigations (Vannote et al. 1980). In regard to health, the highest health results were found
at the south branch whilst the lowest health was identified at Avon Terrace (supporting
hypothesis 1). Low GPP and intermediate ER are indicative of healthier sites, whilst, high
GPP alongside high and low ER were identified to impose poor health results (Young et al.
2008). The lowest health results that were exhibited at Avon Terrace could be attributed to the
highest % of urbanisation found in the catchment that surrounded it. Although the increase of
urban land cover from Dennes Hole down to Avon terrace was small (1%-3%), it has
previously been identified that even just small urbanisation increases can incur detrimental
health impacts (Clapcott et al. 2012). Degradation of the Maitai River in relation to
urbanisation can be linked to the relationships identified in this study, i.e. the significant
positive correlation between metabolism and DIN, the significant negative correlation
between ER and turbidity and the indicated positive relationship between GPP and ER with
flow.
High quantities of impermeable surfaces in urban areas cause increases in surface runoff,
resulting in rapid river recharges (storm surges) (Imberger et al. 2010; Miserendino & Prinzio
2008) and frequent flow alterations (Arnold & Gibbons 1996 in Somers et al. 2013; Striz &
Mayer 2008 in Newcomer et al. 2012; Death 1995). As a consequence excessive soil erosion
can be caused (Blakely & Harding 2005). Because of enhanced soil erosion turbidity and
nutrient concentrations increase; a result of nutrient compounds binding to organic matter and
adsorbing onto soil particles (Somers et al. 2013; Newcomer et al. 2012; Paul & Meyer 2001).
Increases in nutrients, particularly DIN, can result in rapid algal growth enhancing GPP
(Fellows et al. 2006). As a consequence, eutrophication can be caused; a severe river
condition that results in reduced health and thus high river organism mortality (Paul & Meyer
2001; Cox & Moore 2000). But, unlike GPP which only indicates poor health with high GPP
values, ER can indicate poor river health in relation to low (<0.7 gO2m2day-2) and high ER
results (>10.0 gO2m2day-2). Therefore, poor river health can be indicated by high sediment

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loadings (e.g. turbidity) as ER can be considerably reduced (Bunn et al. 1999). For instance,
increases in sediment loadings have been identified to smother the hyporheic zone (Wilson &
Dodds 2009 in Clapcott et al. 2010), reducing ER. Additionally, high abrasion rates, a
consequence of higher flows and increased sediment loadings, can result in further health
decreases as detrimental impacts are imposed onto the biological community. Including
increases in juvenile fish predation, shelter/refuge losses (e.g. large woody debris) (Violin et
al. 2011; Paul & Meyer 2001) and periphyton scouring resulting in community removal
(Uehlinger 2006). Additionally, the frequency of macroinvertebrate catastrophic drift can
magnify. Consequently, food availability to species higher up the food chain can become
reduced as macroinvertebrate communities are removed (Somers et al. 2013; Young & Huryn
1996). But, rises in sediment loads and flow can also cause channel incision. Subsequently,
habitats can become simplified and homogenous, leading to biodiversity reductions (Somers
et al. 2013; Blakely & Harding 2005).
The lower three sites (the Campground, Dennes Hole and Avon Terrace) which exhibited low
metabolism health results also had high proportions of exotic vegetation surrounding them. In
particular the Campground had significantly lower GPP values compared to Avon Terrace
and Dennes Hole. This metabolism difference cannot be attributed to urbanisation because
Dennes Hole and the Campground had the same amount of urbanisation surrounding them.
However, exotic vegetation was identified as being 17% lower at the Campground compared
to the proportions at Avon Terrace and Dennes Hole. This pattern indicates that higher exotic
vegetation is related to higher GPP values and thus river health reductions. However, the
health results for ER continuously changed from poor-satisfactory-healthy between the lower
three sites showing no obvious pattern in regard to exotic vegetation proportions. In relation
to these various metabolism findings it is important to consider than ER and GPP may have
different levels of sensitivity to stressors caused by human disturbances.
The lowest health at Avon Terrace indicated that the interactions of physical, chemical and
biological modifications from different human disturbances imposed at the same time
(urbanisation and exotic vegetation) can magnify and exacerbate river health reductions (Li et
al. 2013; Young et al. 2008). In contrast, the South Branch had a much higher river health,
attributed to lower human disturbances (i.e. human land-uses). However, there were higher
proportions of pasture land surrounding the south branch; pasture land increased by 9% from
Avon terrace to the South Branch. Although agricultural practices have consistently been
shown to have detrimental health impacts (Allan 2004; Sweeney et al. 2004; Church 2002),

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the high health of the South Branch suggests that the agricultural management being executed
in the Maitai catchment is following environmental legislation (e.g. The Resource
Management Act 1991) (Parliamentary Council Office 1991). For instance, Nelson’s resource
management plan from Nelson City Council outlines that activities in or around freshwater
resources have to be consented (Nelson City Council 2015). Additionally, high health results
have previously been identified at agricultural headwater streams in the USA, suggesting that
agricultural management can have a positive impact on river health (e.g. Moore & Palmer
2005).
Furthermore, the GPP and ER temporal results expressed no significant difference over time,
providing evidence that hypothesis two cannot be supported. This contradicts previous
research that identified temperature as an important driving factor of GPP and ER; as
temperature increased, GPP and ER followed (Venkiteswaran et al. 2007; Bott et al. 2006).
However, because this study was only investigated in the summer season, the main
temperature differences that occur between seasons (particularly winter and summer) could
not be detected.

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4.2 Organic Matter Decomposition
The investigation identified that there were spatial differences in OMD within the Maitai
River, as the %CTSL at Avon Terrace was found to be significantly higher than the other four
sites. Alongside this, a consistent satisfactory health was identified across all sites. Therefore,
hypothesis three cannot be supported because there was no conclusive evidence showing that
the South branch had a significantly higher health compared to the rest of the sites. However,
the higher OMD at Avon Terrace could be attributed to the lack of a riparian zone
surrounding this reach; as the other four investigated sites had riparian zones (even though
vegetation type and zone width did vary). The removal of the riparian zone is detrimental to
OMD because once riparian vegetation is cleared light availability increases, subsequently
causing temperature rises (VyŠná et al. 2014; Fellows et al. 2006). Temperature is one of the
main drivers of OMD because higher temperatures stimulate microbial and macroinvertebrate
activity (Collier et al. 2013; Young et al. 2008; Uehlinger 2006). In regard to this, a
significant positive correlation was identified in the Maitai River between fine gravel and
OMD. This can be attributed to fine sediments being able to support higher amounts of
microbes and bacteria (Fellows et al. 2006). Avon Terrace exhibited the highest proportion of
fine gravel as well as expressing the highest OMD, supporting previous suggestions that a
high proportion of fine gravel increases microbial populations causing accelerated OMD.
However, this indication needs to be analysed further because it has previously been
identified that deposited sediment can have higher microbial activity (and potentially higher
OMD potential) than gravel substrate (Clapcott & Barmuta 2010).
Additionally, channel incision (mainly widening of the channel) was observed at Avon
Terrace indicating that flash floods occur frequently. In regard to this, one rapid and extreme
flash flood occurred during the five month investigation on the 1/01/2015. Sudden flash
floods cause exponential decay rates because of extremely high and rapid abrasion rates
(VyŠná et al. 2014; Collier et al. 2013; Clapcott & Barmuta 2010). This could explain why a
temporal difference was found when using the k coefficient (exponential decay) but not when
analysing the %CTSL (linear decay). Furthermore, although a temporal variation was
identified (with the use of the k coefficient), where higher OMD was found in January, it
cannot be conclusively determined if this was the result of higher temperatures or a result of
the flash flood. Therefore, hypothesis four cannot be supported because of a lack of evidence.

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4.3 Macroinvertebrate Communities
Strong support was indicated for hypothesis five as the South Branch exhibited the highest
health in regard to the MCI, QMCI and %EPTtaxa indices. By utilizing these
macroinvertebrate indices it was also clear that river health and macroinvertebrate diversity
decreased (i.e. pollution sensitive taxa decreased) as human disturbance increased. This can
be supported by previous research that found that macroinvertebrate diversities, particularly in
regard to %EPTtaxa, were reduced as water quality decreased (Latha &Thanga 2010); a
particular consequence of urbanisation increases (Moore & Palmer 2005; Hachmoller et al.
1991; Pratt et al. 1981).
However, it was unexpected that some of the sites in close proximity to each other (i.e. South
Branch and site B, Dennes Hole and Avon Terrace) had distinct variations (decreases) in
health. Firstly, %EPTtaxa was found to be significantly lower at Avon Terrace in comparison
to Dennes Hole. This indicates that considerably higher human impacts occurred over a short
distance between the sites. The only increase from Dennes Hole down to Avon Terrace was
the small rise in urbanisation, a land use that can significantly increase nutrient concentrations
(e.g. DIN) (Paul & Meyer 2001). Therefore, the strong negative relationship identified
between DIN and %EPTtaxa and hence the low % EPTtaxa and high DIN concentration at
Avon Terrace suggests the detrimental health impacts caused by urbanisation. Additionally,
the observation of channel incision links urbanisation to the considerable health decrease at
Avon Terrace. Channel incision straightens and widens river reaches, a result of impermeable
surfaces in urban areas causing high quantities of surface runoff (Paul & Meyer 2001). As a
consequence rivers can become recharged almost instantaneously, causing rapid flow
modifications (Imberger et al. 2010; Miserendino & Prinzio 2008) that can produce high
erosional rates over short time frames (Blakely & Harding 2005). These flow alterations and
thus physical river condition modifications are important because they can cause homogeneity
in benthic ecosystems and hence habitat simplification, resulting in a reduction of
macroinvertebrate biodiversity (Somers et al. 2013; Violin et al. 2011). However, it has been
identified that macroinvertebrate losses are complex. ‘Brook et al. (2002)’ found that various
levels of manipulated heterogeneous environments (low to high heterogeneity) (used to
investigate macroinvertebrate rehabilitation) had no impact on macroinvertebrate recoveries.
Therefore, indicating that other variables alongside flow and sedimentation (tested in their
investigation) were having an effect on macroinvertebrates when ecosystem simplification

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was caused. This contradicts a previous study that determined that the hydrological regime
was the main driver of macroinvertebrate communities (Death 1995).
Furthermore, the investigation identified a relatively strong negative correlation between fine
sediment and QMCI scores. This relationship demonstrated that pristine river habitats (high
MCI and QMCI values) contained less fine sediment, indicating that pollution sensitive taxa
reside more readily in gravel, pebble and cobble environments. However, the full extent of
macroinvertebrate variations in different habitats cannot be fully known as the investigation
was only conducted at a reach scale. But, the cause of increased fine sediment loadings, lower
macroinvertebrate indices values and thus lower river health’s (found at the lower sites,
particularly at Avon Terrace) can be the result of higher surface runoffs (a consequence of
impermeable surfaces in urban areas). Excessive surface runoff causes higher terrestrial
erosion, resulting in higher river sediment loadings (fine sediment), which leads to habitat
smothering and alteration (Wood & Armitage 1997).
Another decrease in health over a short distance was identified from the South Branch down
to Site B. At the South Branch water is abstracted to supply Nelson City’s drinking water
(Crowe et al. 2004). Consequently, the river water level can decrease. As a result, the
subsequent river flow alterations can cause detrimental river health impacts at and below the
South Branch (see the ‘Natural flow paradigm’; Poff et al. 1997). As it has been suggested
that structural river components can be altered if flow is modified naturally and by human
disturbances (Fiedler & Zhang 2009; Mathuriau et al. 2008; Hart et al. 2001; Flecker &
Feifarek 1994). To prevent flow alterations causing river health implications Nelson City
Council has opted to using water sourced from the Maitai reservoir to maintain water levels;
the water is added just below the South Branch and just above Site B. The decrease in health
from the South Branch to Site B indicates that the reservoir water being added has caused
substantial river health reductions, considering the short distance between the two sites.
Observations of the reservoir water during the study found that it contained high quantities of
tannins, indicating the presence of physiochemical differences. However, further research is
required to completely understand the impact of reservoir water on the Maitai Rivers health.
This has been has recognised by Nelson City Council as they are aiming to rectify this
problem in the near future (Nelson City Council, B. 2015)
Furthermore, the functional feeding groups identified at the South Branch (control site)
indicated strong evidence that the headwater reach was of a high heterotrophic nature and

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hence relied on allochthonous carbon. The absence of grazers indicated that the South Branch
had low algal cover (low autochthonous carbon). Whereas, the presence of shredders (the
south branch was the only site with this group) suggested this reach particularly relied on
external vegetation inputs such as leaves (allochthonous carbon). This suggests that the RCC,
which also describes headwaters as heterotrophic systems, can be applicable for use in
scientific investigations, particularly if healthy control sites are not available (Vannote et al.
1980).

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5.0 Concluding Remarks
5.1 Investigation Limitations and Further Research
The main limitation of this investigation was the studies duration which only extended over
the summer season. As considerable annual (i.e. seasonal) variations of OMD (Collier et al.
2013; Uehlinger 2006; Young et al. 2008), metabolism (Clapcott & Barmuta 2010; Roberts et
al. 2007; Uehlinger 2006) and macroinvertebrate communities (Yazdian et al. 2014; Stark &
Phillips. 2009; Thompson & Townsend 1999) have been identified. For instance, ‘Alvarez-
Cabria et al. (2010)’ found that the dominant taxa of macroinvertebrate communities changed
seasonally in response to flow variations which can be influenced by natural and human
disturbances. Therefore, further investigation that extends over an annual and inter-annual
duration is preferable.
Additionally, this investigation was limited because it only measured indicators at a reach
scale. Even though patch scales have also been shown to have considerable structural and
functional variations. As a consequence it is recommended to conduct further investigation at
various scales. This would provide a higher comprehensive health investigation with the
incorporation of communities and processes at different scales and habitats, as degradation
responses can vary between scales (e.g. catchment, reach, patch scales) (Bunn et al. 2010;
Clapcott & Barmuta 2010). Furthermore, future research into the Maitai rivers health and
response to human disturbances should include the monitoring of flow, different habitat types,
more in-depth land use distinctions, light availability and cloud cover. All of these should be
collected alongside structural and functional health indicators to increase response
understandings (Clapcott & Barmuta 2010; Roberts et al. 2007; Fellows et al. 2006; Bunn et
al. 1999), aiding in rehabilitation and mitigation strategies.
In regard to the collection of macroinvertebrate communities, this study followed the
recommendations of taking samples only from riffle habitats at each site (Stark & Phillips
2009; Wang et al. 2006). But, variations in the literature (e.g. Collier et al. 2013; Bunn et al.
2010) suggests that it would be beneficial for future Maitai health assessments to monitor
pool, run and riffle habitats. This is because by only sampling riffle habitats other important
members of the macroinvertebrate community may not be represented if they reside in other
habitats. This would be particularly beneficial for the headwater reaches (South Branch and
Site B) as they had highly deep pools covering large areas. However, although this could aid