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New Zealand Journal of Marine and Freshwater Research
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tnzm20
Long-term variability in deposited fine sediment
and macroinvertebrate communities across
different land-use intensities in a regional set of
New Zealand rivers
Noah G. Davis , Roger Hodson & Christoph D. Matthaei
To cite this article: Noah G. Davis , Roger Hodson & Christoph D. Matthaei (2021): Long-term
variability in deposited fine sediment and macroinvertebrate communities across different land-use
intensities in a regional set of New Zealand rivers, New Zealand Journal of Marine and Freshwater
Research, DOI: 10.1080/00288330.2021.1884097
To link to this article: https://doi.org/10.1080/00288330.2021.1884097
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Published online: 15 Feb 2021.
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RESEARCH ARTICLE
Long-term variability in deposited fine sediment and
macroinvertebrate communities across different land-use
intensities in a regional set of New Zealand rivers
Noah G. Davis
a
, Roger Hodson
b
and Christoph D. Matthaei
a
a
Department of Zoology, University of Otago, Dunedin, New Zealand;
b
Environment Southland, Southland,
New Zealand
ABSTRACT
Macroinvertebrate communities in running waters are commonly
used as bioindicators of fine sediment pollution, but few studies
evaluate impacts across multiple years. We used a 5-year dataset
from 46 rivers in Southland, New Zealand to investigate the
consistency of the relationship between deposited fine sediment
and stream macroinvertebrates across three categories of
agricultural land-use intensity (low, medium, and high). We also
compared the performance of four widely used invertebrate
stream health metrics and their recently developed sediment-
specific counterparts. Linear and non-linear regressions were
fitted and effect sizes were interpreted to identify biologically
meaningful relationships (r
2
≥0.1). Sites within medium-intensity
catchments showed the greatest number of such relationships
(29 of 40 cases), compared to low- (8) or high-intensity
catchments (23). Invertebrate metrics responded more frequently,
and mostly negatively, to increasing sediment in medium- and
high-intensity catchments. Overall, sediment-specific metrics
performed better than their widely used counterparts. Our
findings show that land-use intensity influences the multi-year
dynamics of deposited fine sediment and the corresponding
stream invertebrate responses. These temporal dynamics can be
substantial and should be considered in future stream
biomonitoring efforts.
ARTICLE HISTORY
Received 16 October 2020
Accepted 29 January 2021
HANDLING EDITOR
Laura Kelly
KEYWORDS
Stream invertebrates; in-
stream sedimentation;
temporal variation;
freshwater; multi-annual;
biomonitoring
Introduction
Preventing ecological degradation of freshwater ecosystems due to land-based human
activities is a great challenge for environmental managers. To help address this challenge,
stream biomonitoring using benthic macroinvertebrates is a strategy employed in New
Zealand and worldwide (Buss et al. 2015; Wagenhoffet al. 2016). However, such
studies have rarely explored changes over ecologically relevant time periods (Jackson
and Füereder 2006), and erroneous conclusions about stream health may be made if
an inappropriate sampling frequency of invertebrates is used (Collier 2008; Huttunen
et al. 2018; Idígoras Chaumel et al. 2019). This is particularly concerning given that
© 2021 The Royal Society of New Zealand
CONTACT Noah G. Davis davno443@student.otago.ac.nz
Supplemental data for this article can be accessed https://doi.org/10.1080/00288330.2021.1884097
NEW ZEALAND JOURNAL OF MARINE AND FRESHWATER RESEARCH
https://doi.org/10.1080/00288330.2021.1884097
invertebrate community composition can vary considerably between years in streams
impacted by human activities (Feio et al. 2010; Huttunen et al. 2012). Therefore,
without long-term datasets, misleading information regarding ecological stream health
may be used by resource managers.
Fine sediment is a major pollutant of streams and rivers worldwide, often with detri-
mental effects on benthic macroinvertebrate communities (Jones et al. 2012; Davies-
Colley et al. 2015). In New Zealand, field surveys have identified consistent negative
responses of stream macroinvertebrates to increases in deposited fine sediment resulting
from pastoral development (e.g. Niyogi et al. 2007; Townsend et al. 2008; Burdon et al.
2013). Moreover, experiments spanning several spatial scales have established that fine
sediment from agricultural inputs is a ‘master stressor’, owing to its pervasive impacts
on pollution-sensitive macroinvertebrate taxa (see reviews by Scarsbrook et al. 2016; Mat-
thaei and Piggott 2019). In a set of agricultural streams in Canterbury, habitat loss due to
sediment deposition was identified as a key driver affecting pollution-sensitive invert-
ebrates (Burdon et al. 2013). Similarly, Pingram et al. (2019) concluded that improving
in-stream habitat by reducing fine sediment was likely to yield the most widespread
improvement to their biological condition in a region-wide study of Waikato streams
(the spatial scale most relevant to resource managers). While these studies highlight
our understanding of sedimentation effects at various spatial scales, research exploring
temporal variability in fine sediment, and its implications for stream health, remains
limited. However, understanding this variability is important to successfully managing
ecological impacts of sedimentation, particularly because ongoing agricultural develop-
ment has enhanced the delivery of fine sediment to waterways across New Zealand
(Glade 2003; Davies-Colley et al. 2015).
Overland flow is probably the largest source of diffuse pollution of freshwaters in
New Zealand (Howard-Williams et al. 2011), transporting fine sediment from terres-
trial systems into streams during rainfall (Merritt et al. 2003; Dymond et al. 2017).
Consequently, sediment delivery into streams is highly flow-dependent, yet non-
linear, episodic and hard to predict (Wohl et al. 2015). For example, within a large
agricultural catchment in France, suspended sediment transport was shown to be
highly variable across several temporal scales due to rainfall and floods (Oeurng
et al. 2010). New Zealand studies, although rare, have used paired catchment
approaches and determined that, on average, pasture catchments can export 3 times
(Quinn and Stroud 2002) or 1.6 times (Hughes et al. 2012) more fine sediment than
mature forest catchments. Given the known temporal variability in suspended sedi-
ment concentrations, it is important to also consider the variability of deposited fine
sediment over time. However, few studies worldwide have investigated this variability,
let alone its impacts on stream invertebrate communities. In one such study, small
colonisation columns were exposed in two UK streams for 126 days (Mathers et al.
2017), with two sediment levels (heavily sedimented versus control). In two New
Zealand experiments (Matthaei et al. 2006; Ramezani et al. 2014), fine sediment was
added to 50-m stream reaches and sediment levels were also monitored in control
reaches, but these studies ran only for 35 or 63 days. Arguably, all three experiments
were thus conducted at spatial and/or temporal scales of limited relevance for decision
making by freshwater managers.
2N. G. DAVIS ET AL.
Resource managers have to prioritise mitigation actions that are both cost-effective
and ecologically beneficial (Turak and Linke 2011). However, some of the current phys-
ical or visual methods for assessing deposited fine sediment (see Clapcott et al. 2011 for
New Zealand) can be time-consuming and expensive, making it unrealistic for most
freshwater managers to conduct more than one sampling campaign per year. Therefore,
overseas research has identified the need for a new approach that utilises benthic macro-
invertebrate metrics as a proxy for the extent of in-stream sedimentation (Extence et al.
2013; Turley et al. 2014; Extence et al. 2017). In New Zealand, a new set of sediment-
specific invertebrate metrics was developed by Clapcott et al. (2017). These metrics
have the potential to isolate impacts of fine sediment on ecological stream health, allow-
ing sediment-specific mitigation strategies to be effectively targeted. As macroinverte-
brate biomonitoring is mandated in the National Policy Statement for Freshwater
Management (MfE 2020), these metrics would also provide a national standard for asses-
sing fine sediment. Further, where biomonitoring data from previous years exist, ‘back
forecasting’could be undertaken, by identifying long-term trends in past sediment
levels and using them to fill current knowledge gaps and help predict future trends.
Against this scientific background, our first aim was to investigate the influence of
catchment land-use intensity on the relationship between fine sediment and macroinver-
tebrate communities assessed annually across five years in a regional dataset of waterways
in Southland, New Zealand. Our second aim was to compare the ability to detect sedi-
mentation impacts of sediment-specific stream invertebrate metrics recently developed
for New Zealand by Clapcott et al. (2017) and existing, widely used invertebrate
stream health metrics. Both aims were addressed using a unique dataset that provided
a long-term perspective on regional deposited fine sediment levels; an approach that,
to our knowledge, has not yet been explored in New Zealand or elsewhere. We tested
four hypotheses regarding the multi-year relationships between sediment levels and
invertebrate stream health metrics in waterways draining catchments of low, medium
or high land-use intensity:
(1) low-intensity catchments will show few, weak or no relationships between invert-
ebrate metrics and in-stream sediment levels,
(2) medium-intensity catchments will consistently show many invertebrate-sediment
relationships, but these will vary in strength across years,
(3) high-intensity catchments will show similar patterns as low-intensity ones, and
(4) across all land-use intensities, the newly developed sediment-specific invertebrate
metrics will respond more strongly to deposited sediment than their long-established
non-sediment-specific counterparts.
The rationales behind these hypotheses are as follows: Representing a relatively low
risk of sediment transfer from terrestrial to freshwater systems, streams within low-
land-use intensity catchments (H1) should have consistently low annual sediment
levels, regardless of natural variability in environmental conditions (see e.g. tussock
streams in Matthaei et al. 2006). In streams draining high land-use intensity catchments
(H3), by contrast, consistently high sediment levels are expected given the large pro-
portion of the catchment that can continually or periodically produce sediment inputs
to waterways (e.g. deer farming streams in Matthaei et al. 2006). Thus, invertebrate
NEW ZEALAND JOURNAL OF MARINE AND FRESHWATER RESEARCH 3
communities within such streams should remain relatively stable, showing no relation-
ship to sediment levels as there is no sediment gradient to respond to. Finally, streams
within medium land-use intensity catchments (H2) should show the greatest variability
in sediment levels. This is because variability in rainfall, and in turn surface runoffand
stream flows, are expected to alter in-stream sediment levels to a larger degree compared
to low and high land-use intensity catchments (e.g. dairy farming streams in Matthaei
et al. 2006 and Ramezani et al. 2014). Therefore, invertebrates should show more
varied responses to sediment levels, depending upon the extent to which they fluctuate
from year to year.
Methods
Field sites
Our study utilised aquatic invertebrate and deposited sediment data collected in the long-
term biomonitoring programme of Environment Southland (Southland’s regional
council). This programme includes the collection of environmental stressor and ecologi-
cal response data from up to 102 sites across four large agricultural river catchments
(Waiau, Aparima, Oreti, Mataura) and a number of smaller coastal rivers and streams
throughout the Southland region of New Zealand. Monitoring sites were selected to rep-
resent broad gradients of environmental stressors (pristine to highly impacted), and to
include regionally important main-stem river sites. A primary programme focus is to
gain an understanding of the region-wide impacts of deposited fine sediment on water
quality.
Forty-six river sites (Table S1, Supplementary Material) sampled consecutively for
both deposited fine sediment and benthic macroinvertebrates for 5 years were chosen
to capture annual variability in sediment and invertebrate communities in Southland.
This sampling period represents the minimum time needed to interpret results from a
meaningful range of environmental conditions (Jackson and Füereder 2006). Each site
was sampled annually during baseflow conditions from 2015 to 2019, primarily during
Austral summer. Sediment and invertebrate data were collected simultaneously.
Baseflow conditions were discharges below a threshold of 3 times the annual median
flow. If this threshold was exceeded, this resulted in a 2-week stand-down period
before sampling was undertaken. The stand-down reduced the influence of floods,
which are known to be an important driver determining lotic invertebrate assemblages
(e.g. Resh et al. 1988). All sites were in hard-bottomed waterways, although they
differed somewhat in catchment geology. However, the relationship between land use
and deposited fine sediment did not depend on catchment geology in a closely related
set of 43 Southland streams (Wagenhoffet al. 2011); thus, the influence of geology on
the findings of the present study is probably negligible.
While nutrient enrichment is another well-known agricultural stressor in New
Zealand (e.g. review by Scarsbrook et al. 2016), this stressor was not considered in our
study because the Environment Southland dataset did not contain nutrient data from
all sites in all years. Given the importance of fine sediment as a stressor and the lack
of long-term datasets both nationally and internationally, we did not want to compro-
mise the large spatial and multi-year temporal nature of our study. Moreover, two
4N. G. DAVIS ET AL.
earlier regional-scale surveys that used data from Southland waterways collected with a
‘snapshot’approach (Wagenhoffet al. 2011; Macher et al. 2016) had already included
nutrients as a stressor.
Sampled sites differed in the proportions of their upstream catchments dedicated to
agriculture. As a proxy for land-use intensity, we used the proportion of the upstream
catchment dedicated to pastoral land (%USPasture), which was obtained from the
Department of Conservation’s Freshwater Ecosystems of New Zealand (FENZ) database.
The proportions of the upstream catchment area with agricultural land-cover have con-
sistently been correlated positively with in-stream contaminant concentrations and nega-
tively with invertebrate stream health measures (e.g. Wagenhoffet al. 2011; Lange et al.
2014; Larned et al. 2020). Sites ranged from 0% to 98%USPasture, ensuring annual varia-
bility in sediment and invertebrate metrics could be compared across the full range of
catchment land-use intensity in Southland. To test hypotheses H1–H3, sites were
assigned into low, medium, and high land-use intensity categories based on the pro-
portion of pasture upstream. Low land-use intensity sites (n= 15) had 0%–33% of the
upstream catchment in pasture. The equivalent percentages were 35%–65% for
medium-intensity sites (n= 16) and 66%–98% for high-intensity sites (n= 15).
Biological sampling
At each site, macroinvertebrates were sampled from a hard-bottomed reach of riffle
habitat following standard protocols for wadeable streams in New Zealand (Stark et al.
2001). Five Surber samples (0.5-mm mesh net, 25 × 25 cm frame) were collected from
locations to best represent the entire riffle habitat, by accounting for the various water
depths, flow velocities and substratum types present. The material from these samples
was pooled, resulting in one composite sample per site. Each sample was stored in a
1-L PET container and preserved in a final concentration of 70% ethanol for later pro-
cessing at Ryder Consultancy, Dunedin, New Zealand. Samples were processed for
taxon identification and relative abundances following the 200 fixed-count protocol P2
in Stark et al. (2001). The first 200 individuals per sample were counted and identified,
then the entire sample was scanned for rare taxa. Further, total invertebrate abundance
in each sample was estimated using a weighting factor based on the proportion of sample
processed during the 200-count. Invertebrates were identified under a dissecting micro-
scope (10–40×) using keys from Winterbourn et al. (2006).
Fine sediment
Fine sediment trapped in the top layers of the stream bed (suspendable inorganic sedi-
ment, SIS) was measured using the quantitative ‘Quorer’technique (sediment assessment
method 4 in Clapcott et al. 2011). At each site, Quorer sampling (six samples per site, plus
one control) was performed in the run habitat immediately upstream of the riffle where
macroinvertebrates had been collected (see also next section).
At each sampled streambed patch, a cylindrical tube (length 60 cm, diameter 30 cm)
was embedded into the substratum. Using a broom handle marked with 1-cm gradations,
five water depths were measured inside the cylinder to obtain an average depth. Using the
broom handle, the bed within the cylinder was stirred for 15 s and a slurry sample
NEW ZEALAND JOURNAL OF MARINE AND FRESHWATER RESEARCH 5
(sediment and water) was collected immediately from the water surface using a 200-mL
PET container. With the cylinder still sealed, five depth measurements were taken within
it to determine the average stirred depth. This procedure was repeated at five more bed
patches within the same run, moving upstream. One background water sample per site
was collected and, together with the six Quorer samples, frozen on the day of collection.
Samples were analysed at Hills Laboratory (Hamilton, New Zealand) following the pro-
tocols in Clapcott et al. (2011).
Biological response variables
Data from riffles and adjacent runs at 33 Southland river sites sampled by Environment
Southland staffin summer 2019, using the field and laboratory protocols described above,
showed that values for the NZ Macroinvertebrate Community Index (MCI) from riffles
were strongly correlated with those from adjacent runs (linear regression, r
2
= 0.59; see
Figure S1, Supplementary Material). Thus, it is reasonable to compare sediment data
obtained from a run with invertebrate data collected from the adjacent riffle, as in the
present study.
For each site, we determined MCI, semi-quantitative MCI (SQMCI), percentage con-
tribution of larval Ephemeroptera, Trichoptera and Plecoptera (EPT) taxa to total
number of taxa per sample (%EPT taxa), and percentage contribution of EPT taxa to
total number of individuals per sample (%EPT taxa abundance). EPT taxa are commonly
used worldwide to assess ecological stream health due to their sensitivity to a wide range
of pollutants. While MCI and SQMCI are only applicable in a New Zealand context, they
are complemented by the EPT metrics, allowing our findings to be interpreted in an
international context. Ryder Consultancies provide SQMCI but no QMCI calculations
for processed invertebrate samples to Environment Southland, thus no QMCI data
were available. However, Stark (1998) concluded in the paper introducing the SQMCI
that this index ‘effectively produces the same assessment’as the QMCI.
A selection of four sediment-specific macroinvertebrate metrics, developed by Clap-
cott et al. (2017) (see below), were also calculated at each site. These were the sedi-
ment-specific Macroinvertebrate Community Index (Sediment-MCI), the sediment-
specific Quantitative Macroinvertebrate Community Index (Sediment-QMCI), percen-
tage contribution of sediment-sensitive taxa to total number of taxa per sample (%
Decreaser richness), and percentage contribution of sediment-sensitive taxa to total
number of individuals per sample (%Decreaser abundance). Note that Clapcott et al.
(2017) did not develop a sediment-specific SQMCI.
In Clapcott et al. (2017), a wide range of invertebrate taxa were identified as either
being tolerant or sensitive to fine sediment pollution. Taxa were deemed to be sensitive
to sediment if their modelled response shape decreased across the sediment gradient
(‘Decreasers’), or tolerant if they increased across the gradient (‘Increasers’). To
further discriminate between different degrees of sensitivity or tolerance, a 1–10
scale was assigned. Tolerance values for decreasers ranged from 10 to 6, with 10
being assigned to the most sensitive taxa. Tolerance values for increasers ranged
from 1 to 5, with 1 assigned to the most tolerant taxa. For calculating the sediment-
specific metrics above, we obtained tolerance values and response group (Increaser,
Decreaser) from Clapcott et al. (2017). Log-scale tolerance values were used as they
6N. G. DAVIS ET AL.
provided a more even distribution across the tolerance range of 1–10 than the raw scale
in Clapcott et al. (2017).
Data analysis
To assess the relationships between deposited fine sediment (SIS g m
−2
) and stream
health metrics when testing hypotheses H1–H3, each land-use category (low, medium,
high) and year (2015–2019) were treated separately. Linear and non-linear regressions
were fit to the eight invertebrate metrics and SIS values for each land-use category and
year. When testing our hypotheses, we focussed on the magnitude of effect sizes,
rather than on their statistical significance. According to the widely-cited review by
Nakagawa and Cuthill (2007), using standardised effect sizes is a more effective way to
assess the likely biological importance of a relationship than interpreting differences
based on p-values. To identify biologically meaningful relationships between sediment
and invertebrate metrics, we focussed on those with effect sizes of ≥0.1. This value rep-
resents a benchmark for what can be considered the minimum strength for a relationship
to be biologically meaningful (Nakagawa and Cuthill 2007).
To test H4, the strengths of the relationships between fine sediment and the sediment-
specific stream health measures were compared to their non-sediment-specific counter-
parts. The following comparisons were made: MCI and Sediment-MCI, SQMCI and
Sediment-QMCI (because no Sediment-SQMCI exists, see above), %EPT taxa and %
Decreaser richness, and %EPT abundance and %Decreaser abundance. Sediment-
specific metrics were deemed to have performed better if their effect sizes were larger
compared to their counterparts.
In 2017, an abnormally high SIS value (6907 g m
−2
) was recorded for one site, the
Waikaia River at Waikaia (%USPasture = 16%, Low land-use category). Sampling was
done in a run habitat, which had stagnant water at the time due to an exceptionally
dry summer (N. Hughes, Environment Southland, personal communication). Conse-
quently, this SIS value was deemed not to be representative of the site and this individual
data point was removed from the analysis. Several of the curvilinear relationships fitted to
our data contained one highly influential data point (see Figure S2, Supplementary
Material). However, the key findings of our paper remained unchanged when these
data points were removed to test their potential impact. Further, unlike for the above-
mentioned SIS value, we had no a priori reason for omitting these data points, thus
they were all retained.
To complement the analyses run for H1–H3, equivalent regressions were also fit for all
46 sites combined (without splitting the data according to land-use intensity categories)
and each invertebrate metric, again separately for each year. Biologically meaningful
relationships were interpreted following the protocols described above.
Results
Across all five years and the eight response variables, 40 regressions were computed in
each of the three %USPasture categories. The low %USPasture bin contained eight
relationships strong enough to be biologically meaningful (effect size r
2
≥0.10). The
NEW ZEALAND JOURNAL OF MARINE AND FRESHWATER RESEARCH 7
medium %USPasture bin showed 29 such relationships, while the high %USPasture bin
comprised 23.
MCI (Figure 1) and Sediment-MCI (Figure 2), respectively, showed eight and seven
relationships with SIS (of 15 possible ones, across all five years and all three bins)
strong enough to be biologically meaningful. In the low %USPasture bin, no meaningful
relationships with SIS were observed for the MCI, whereas a negative linear relationship
(r
2
= 0.18) occurred in 2019 for the Sediment-MCI. In the medium %USPasture bin,
meaningful relationships were present across all five years when using the MCI. Negative
linear relationships were observed in 2016 (r
2
= 0.13), 2018 (r
2
= 0.32) and 2019 (r
2
=
0.27), while non-linear relationships occurred in 2015 (negative; r
2
= 0.34) and 2017
Figure 1. Linear and non-linear relationships between site measurements of deposited fine sediment
(SIS g m
−2
) and MCI scores for each sampling year across low (n= 15), medium (n= 16), and high (n=
15) %USPasture categories. Note: n= 14 in the low %USPasture for 2017 where one abnormally high
SIS value was removed (see Methods). Regression lines and r
2
-values are shown only for biologically
meaningful relationships (r
2
≥0.10).
8N. G. DAVIS ET AL.
(subsidy-stress: initial increase then decline; r
2
= 0.11). The Sediment-MCI detected three
meaningful relationships: negative linear relationships in 2016 (r
2
= 0.11) and 2019 (r
2
=
0.37), and a negative, slightly non-linear relationship in 2015 (r
2
= 0.14). At high %
USPasture, MCI and sediment-MCI performed similarly, with three meaningful relation-
ships each detected in 2015, 2017, and 2018. Response directions and shapes were also
similar. In 2017, negative linear relationships were observed for both MCI (r
2
= 0.41)
and Sediment-MCI (r
2
= 0.51). In 2015 and 2018, negative non-linear relationships
were present for MCI (r
2
= 0.12 and 0.27, respectively) and Sediment-MCI (r
2
= 0.11
and 0.27).
SQMCI (Figure 3) and Sediment-QMCI (Figure 4), respectively, showed eight and
seven biologically meaningful relationships with SIS. At low %USPasture, one negative
linear relationship occurred in 2019 for both SQMCI (r
2
= 0.12) and Sediment-QMCI
Figure 2. Linear and non-linear relationships between site measurements of deposited fine sediment
(SIS g m
−2
) and Sediment-MCI scores for each sampling year across low (n= 15), medium (n= 16), and
high (n= 15) %USPasture categories. See Figure 1 for further details.
NEW ZEALAND JOURNAL OF MARINE AND FRESHWATER RESEARCH 9
(r
2
= 0.18), with similar response direction and shape. At medium %USPasture, mean-
ingful relationships were observed in four years for SQMCI and three for Sediment-
QMCI. For the SQMCI, negative linear relationships occurred in 2016 (r
2
= 0.28) and
2018 (r
2
= 0.18), and negative non-linear relationships in 2015 (r
2
= 0.11) and 2019 (r
2
= 0.23). For the Sediment-QMCI, a negative linear relationship was present in 2016
(r
2
= 0.30), with non-linear relationships (subsidy-stress) occurring in 2017 (r
2
= 0.12)
and 2019 (r
2
= 0.51). At high %USPasture, SQMCI and Sediment-QMCI performed
similarly, each detecting a meaningful relationship in 2015, 2017, and 2018. In 2017, a
negative linear relationship was observed for SQMCI (r
2
= 0.22) and Sediment-QMCI
(r
2
= 0.43). In 2015 (negative) and 2018 (subsidy-stress), non-linear relationships were
found for SQMCI (r
2
= 0.14 and 0.11, respectively) and Sediment-QMCI (r
2
= 0.11
and 0.15).
Figure 3. Linear and non-linear relationships between site measurements of deposited fine sediment
(SIS g m
−2
) and SQMCI scores for each sampling year across low (n= 15), medium (n= 16), and high
(n= 15) %USPasture categories, See Figure 1 for further details.
10 N. G. DAVIS ET AL.
%EPT abundance (Figure 5) and %Decreaser abundance (Figure 6), respectively,
showed five and six biologically meaningful relationships with SIS. At low %USPasture,
no meaningful relationships were detected for %EPT abundance, whereas a negative
linear relationship was found in 2019 (r
2
= 0.11) for %Decreaser abundance. At
medium %USPasture, meaningful relationships were identified in 2015, 2016 and 2018
for both metrics, with comparable response directions and shapes. In 2015, a negative
non-linear relationship occurred for %EPT (r
2
= 0.10) and %Decreaser abundance (r
2
= 0.15). Linear negative relationships were observed in 2016 and 2019 for %EPT abun-
dance (r
2
= 0.32 and 0.10, respectively) and %Decreaser abundance (r
2
= 0.32 and
0.15). At high %USPasture, two meaningful relationships each were found for %EPT
and %Decreaser abundance. In 2017, a negative linear relationship was observed for
both %EPT abundance (r
2
= 0.28) and %Decreaser abundance (r
2
= 0.40). In 2018, %
Figure 4. Linear and non-linear relationships between site measurements of deposited fine sediment
(SIS g m
−2
) and Sediment-QMCI scores for each sampling year across low (n= 15), medium (n= 16),
and high (n= 15) %USPasture categories. See Figure 1 for further details.
NEW ZEALAND JOURNAL OF MARINE AND FRESHWATER RESEARCH 11
EPT abundance detected a non-linear relationship (subsidy-stress; r
2
= 0.13), whereas in
2015%Decreaser abundance detected a negative linear relationship (r
2
= 0.10).
%EPT richness (Figure 7) and %Decreaser richness (Figure 8), respectively, showed
ten and nine biologically meaningful relationships with SIS. At low %USPasture, such
relationships were detected in 2017 and 2019 by both metrics. In 2019, negative linear
relationships were found for both metrics (r
2
= 0.11 in both cases). In 2017, a negative
non-linear relationship was identified by %EPT richness (r
2
= 0.11), whereas %Decreaser
richness identified a negative linear relationship (r
2
= 0.21). At medium %USPasture, %
EPT and %Decreaser richness performed similarly, each detecting a meaningful relation-
ship in 2015, 2016, 2018, and 2019. Response directions and shapes were also similar. In
2016, a non-linear relationship occurred for both %EPT (subsidy-stress; r
2
= 0.31) and %
Decreaser richness (negative; r
2
= 0.35). In 2015, 2017, and 2018, negative linear
Figure 5. Linear and non-linear relationships between site measurements of deposited fine sediment
(SIS g m
−2
) and %EPT abundance for each sampling year across low (n= 15), medium (n= 16), and
high (n= 15) %USPasture categories. See Figure 1 for further details.
12 N. G. DAVIS ET AL.
relationships were present for %EPT (r
2
= 0.14, 0.10, and 0.28, respectively) and %
Decreaser richness (r
2
= 0.31, 0.14, 0.25). At high %USPasture, %EPT richness and %
Decreaser richness, respectively, showed four and three meaningful relationships. In
2015 and 2017, negative linear relationships were found using %EPT richness (r
2
=
0.12, 0.50) and %Decreaser richness (r
2
= 0.10, 0.58). Non-linear relationships were
present in 2016 (peaking at intermediate SIS levels) and 2018 (subsidy-stress) using %
EPT richness (r
2
= 0.13, 0.42), whereas one non-linear relationship (subsidy-stress)
was identified in 2018 using %Decreaser richness (r
2
= 0.11).
In total, there were 25 cases (out of a possible 60) where both a widely used metric and
its sediment-specific counterpart detected a meaningful relationship in the same year and
land-use category. Of these, sediment-specific metrics had a stronger relationship with
SIS in 15 cases (n= 2, 8, and 5 for low, medium, or high land-use intensity), compared
Figure 6. Linear and non-linear relationships between site measurements of deposited fine sediment
(SIS g m
−2
) and %Decreaser abundance for each sampling year across low (n= 15), medium (n= 16),
and high (n= 15) %USPasture categories. See Figure 1 for further details.
NEW ZEALAND JOURNAL OF MARINE AND FRESHWATER RESEARCH 13
to seven (n= 0, 3, 4) for the widely used metrics (in three cases performance was the
same). Overall detection frequency of meaningful relationships across all years and
land-use categories was slightly higher for the widely used (n= 31) than for the sedi-
ment-specific metrics (n= 29).
In the regression analyses for all 46 sites combined within each sampling year,
response shapes were generally similar to those where land-use intensity categorisation
was used; however, r
2
-values for the combined dataset (Figures S3 and S4; see Sup-
plementary Material for descriptions of main patterns) were often lower than in at
least one of the three corresponding bins (Figures 1–8). Thus, categorising sites based
on %USPasture did not diminish the overall relationships present. Rather, this approach
allowed identifying at which land-use intensity the greatest number, and the strongest,
biologically meaningful relationships occurred.
Figure 7. Linear and non-linear relationships between site measurements of deposited fine sediment
(SIS g m
−2
) and %EPT richness for each sampling year across low (n= 15), medium (n= 16), and high
(n= 15) %USPasture categories. See Figure 1 for further details.
14 N. G. DAVIS ET AL.
Discussion
Invertebrate relationships to fine sediment at different land-use intensities
Strength and frequency of biologically meaningful relationships between deposited fine
sediment and macroinvertebrate stream health metrics differed considerably across
our three agricultural land-use intensity classes. Because we expected in-stream sediment
levels to remain relatively low and stable in low-intensity catchments, we had predicted
few or no relationships between invertebrate metrics and sediment levels (H1). Both
expectations were supported. Across all five sampling years, in-stream sediment levels
remained low, rarely exceeding 1000 g SIS m
−2
, and few biologically meaningful relation-
ships occurred within this land-use category. MCI and %EPT abundance, two widely
used stream health metrics, both showed no meaningful relationship to SIS in any
Figure 8. Linear and non-linear relationships between site measurements of deposited fine sediment
(SIS g m
−2
) and %Decreaser richness for each sampling year across low (n= 15), medium (n= 16), and
high (n= 15) %USPasture categories. See Figure 1 for further details.
NEW ZEALAND JOURNAL OF MARINE AND FRESHWATER RESEARCH 15
sampling year. The remaining metrics either showed one (Sediment-MCI, SQMCI, Sedi-
ment-QMCI, %Decreaser abundance) or two (%EPT richness, %Decreaser richness)
meaningful relationships, although all these were weak (r
2
≥0.1 but <0.3; Nakagawa
and Cuthill 2007). It is reasonable to assume that sites within low land-use intensity
catchments have deposited fine sediment levels that generally remain relatively stable
through time, not just from year to year. Therefore, when managers consider how best
to distribute biomonitoring effort, less frequent in-stream sediment sampling may be
warranted in these catchments. Instead, more effort could be directed towards locations
with more variable water quality patterns (discussed below).
Believing that sites within medium land-use intensity catchments had the greatest
potential for fluctuating in-stream sediment levels, we had anticipated many relation-
ships, of varying strength, to occur here between invertebrate metrics and sediment
levels (H2). Our results clearly supported this hypothesis. Detection frequency across
sampling years in medium-intensity catchments was high for all invertebrate metrics,
each finding either three (n= 4), four (n= 3), or five (n= 1) biologically meaningful
relationships out of a possible five. This high detection frequency was probably related
to the wider, more variable spread of in-stream sediment levels compared to low-inten-
sity catchments, which consistently provided a wide sediment gradient for invertebrates
to respond to. Further, these meaningful relationships varied considerably in strength,
with weak (n= 19), intermediate (n= 9), and strong (n= 1) relationships all being ident-
ified (r
2
≥0.1, 0.3, 0.5, respectively; Nakagawa and Cuthill 2007). Thus, paralleling the
more variable in-stream sediment levels, invertebrate community responses to these sedi-
ment levels were also more variable from year to year compared to streams within low-
intensity catchments.
Interestingly, high land-use intensity catchments displayed patterns resembling those
in medium-intensity catchments. We had expected to find meaningful relationships
similar in frequency and strength to those in low-intensity catchments (H3); however,
our results refuted this hypothesis. Detection frequency of meaningful relationships by
all invertebrate metrics in high land-use catchments was more similar to medium-inten-
sity than low-intensity catchments, each showing either two (n= 2), three (n= 5), or
four (n= 1) meaningful relationships with SIS. These varied also similarly in strength
as in medium-intensity catchments, with weak (n= 15), intermediate (n= 5), and
strong (n= 3) relationships occurring.
Our findings also demonstrate that deposited fine sediment levels are not necessarily
consistently high in high-intensity catchments. Rather, as shown in our five-year regional
study, SIS levels of high land-use intensity catchments during summer can range from
levels expected for near-pristine catchments to levels expected for heavily impacted
(high-intensity) catchments. A possible explanation for this surprisingly large range is
that stream order, annual flooding regime, and channel slope can all influence in-
stream fine sediment dynamics. Thus, in a ‘snapshot’survey of 43 Southland waterways,
Wagenhoffet al. (2011) found positive relationships between SIS and % catchment runoff
from pasture for stream orders 4–6, but not for 3rd-order streams. Further, Naden et al.
(2016), using measurements of deposited fine sediment from 230 agricultural streams
across England and Wales, found stream power (calculated using estimated median
annual flood and channel slope) to be the most effective predictor of sediment standing
stocks. These findings imply smaller and/or low-gradient streams are more likely to be
16 N. G. DAVIS ET AL.
impacted by sedimentation because of a decreased ability to re-mobilise deposited sedi-
ment compared to larger and/or steeper streams. Stream gradients were unavailable in
our study and stream order was not included as a predictor due to the fairly small
sample size in each land-use category. However, stream orders varied from 3 to 6 in
high-intensity catchments; thus, individual sites within this category may have differed
in their ability to flush out fine sediment. This explanation suggests that flow variability
influences in-stream sediment levels not just in medium-intensity but also in high-inten-
sity catchments. Consequently, flow variability may to some extent override the impor-
tance of catchment land-use in determining deposited sediment levels, as also observed
by Naden et al. (2016) in their set of agricultural streams.
Most of the meaningful relationships between invertebrate metrics and SIS within our
low-, medium- or high-intensity catchments were straightforwardly negative (8 of 8, 25
of 29, and 15 of 23, respectively), in agreement with the bulk of the published literature
on fine sediment effects on invertebrate stream health metrics in New Zealand and inter-
nationally (see reviews by Jones et al. 2012; Scarsbrook et al. 2016; Matthaei and Piggott
2019). However, some subsidy-stress patterns were identified for medium-intensity (n=
4) and high-intensity (n= 8) catchments. While less common than subsidy-stress
responses to nutrient enrichment, such responses to elevated fine sediment have also
been observed for the caddis larva Pycnocentrodes spp. (an EPT taxon) and larval
elmid beetles in Wagenhoffet al.’s(2011) survey of Southland waterways, and for total
invertebrate density, community evenness and four common taxa in a stream mesocosm
experiment in Otago, New Zealand, where invertebrate responses were assessed over
broad gradients of fine sediment (Wagenhoffet al. 2012).
At low levels, slight increases in deposited sediment are thought to provide additional
habitat for some burrowing taxa, while still providing enough sediment-free refugia for
sediment-sensitive taxa. In our regional data set from Southland, all initial subsidy
responses became negative above ∼1000 g SIS m
−2
. Interestingly, the SIS range of 0–
1000 g m
−2
was also indicative of streams within low-intensity catchments, where the
fewest adverse effects of sediment on invertebrate metrics were observed. Therefore, at
such relatively low sediment levels, it is plausible that sediment-sensitive invertebrates
were able to persist from year to year even when faced with slight increases in sediment
because of available refugia. While investigating the availability of suitable habitat was
beyond the scope of our study, the observed SIS values and the subsidy-stress responses
in both medium- and high-intensity catchments illustrate that some streams within these
land-use categories can also have SIS levels low enough to cause no adverse effects on
stream invertebrate communities, similar to the situation in near-pristine catchments.
Evaluation of sediment-specific invertebrate metrics
To our knowledge, this study represents the first instance where a completely indepen-
dent dataset has been used to evaluate the performance of the new set of sediment-
specific invertebrate stream health metrics developed by Clapcott et al. (2017). Given
that the four widely used stream health indices (MCI, SQMCI, %EPT taxa, %EPT abun-
dance) we employed are known to respond to many environmental stressors, we had
expected their sediment-specific counterparts (Sediment-MCI, Sediment-QMCI, %
Decreaser richness, %Decreaser abundance) to respond more strongly to deposited
NEW ZEALAND JOURNAL OF MARINE AND FRESHWATER RESEARCH 17
fine sediment across all years and land-use intensities (H4). Indeed, stronger responses
(based on r
2
-values) were observed more often for the sediment-specific metrics, in
keeping with this hypothesis.
MCI versus Sediment-MCI was the only case where the widely used metric outper-
formed its sediment-specific counterpart. Although Sediment-MCI found an additional
relationship at low land-use intensity, this was the only relationship detected, giving little
weight to this finding. At medium land-use intensity, MCI detected more meaningful,
and often stronger, relationships (n= 5) than Sediment-MCI (n= 3), while both
metrics performed similarly at high intensity. By contrast, the Sediment-QMCI per-
formed better than the SQMCI. Detection frequency was the same at low intensity (n
= 1), but Sediment-QMCI showed the stronger relationship with SIS. At medium inten-
sity, detection frequency was higher for SQMCI (n= 4) than Sediment-QMCI (n= 3).
Further, where both metrics detected a meaningful relationship in the same year (n=
2), Sediment-QMCI showed stronger correlations with sediment both times. At high
intensity, detection frequency was the same for both metrics (n= 3), but Sediment-
QMCI performed better in all cases. These findings suggest that Sediment-QMCI
could be a more effective predictor for in-stream sediment levels than Sediment-MCI
(which performed less well than the traditionally used MCI).
Identifying the proportions of sediment-sensitive taxa proved to be particularly well-
suited for detecting sedimentation impacts. In low-intensity catchments, %Decreaser
abundance detected one meaningful relationship with SIS where %EPT abundance did
not. In medium-intensity catchments, detection frequency was the same (n= 3) but %
Decreaser abundance showed a stronger correlation with SIS in two instances (in one
case performance was the same). In high-intensity catchments, each metric detected
two meaningful relationships, one of these in the same year, where %Decreaser abun-
dance again showed a stronger correlation. For %EPT richness and %Decreaser richness,
detection frequency was the same in low- (n= 2) and medium-intensity (n= 4) catch-
ments, but %Decreaser richness performed better in one case in low-intensity catchments
(in the other case performance was the same), and in three cases at medium land-use
intensity. Only in high-intensity catchments did %EPT richness detect more meaningful
relationships (n= 4) than %Decreaser richness (n= 3) and, where both detected a mean-
ingful pattern in the same year (n= 3), %EPT richness performed better in two cases.
Overall, therefore, both %Decreaser metrics mostly performed better than their widely
used counterparts, highlighting their potential for future biomonitoring.
Few sediment-specific invertebrate metrics exist elsewhere; however, Extence et al.
(2013) developed one such metric for British streams called ‘PSI’(Proportion of Sedi-
ment-sensitive Invertebrates). PSI assigns macroinvertebrates to one of four sediment
sensitivity ratings with specific abundance-weighted scores, which are used to calculate
a PSI score and interpreted to define the degree of sedimentation at a site. Turley et al.
(2014) evaluated the performance of PSI using data from 835 reference-condition tem-
perate streams and rivers. They found PSI was more highly correlated with fine sediment
(measured as the percentage of the substratum consisting of sand, silt and clay) compared
to %EPT abundance and %EPT richness. Similarly, in our study, we found stronger cor-
relations with sediment for %Decreaser abundance than for %EPT abundance in four
cases (Low 2019, Medium 2015, 2019, High 2017), and for %Decreaser richness than
for %EPT richness in two cases (Low 2017, Medium 2015).
18 N. G. DAVIS ET AL.
Conclusions
By categorising sites based on %USPasture, rather than treating all 46 sites collectively
(see Suplementary Material), we could determine that the greatest number of biologically
meaningful relationships between invertebrate stream health metrics and SIS occurred
within the medium land-use category and the fewest within the low-land-use category.
Interestingly, sites within the high land-use category showed many more, and often
stronger, relationships than initially expected. Overall, invertebrate metrics responded
more frequently to deposited fine sediment in medium- and high-intensity catchments.
Our findings imply that at medium and high land-use intensity sites, greater variability in
in-stream deposited fine sediment and macroinvertebrate communities can be expected.
Consequently, more biomonitoring effort should be directed towards such sites than to
sites within low land-use intensity catchments. This approach would ensure the full range
of variability in deposited fine sediment is captured and allow for the most accurate con-
clusions about stream health to be made.
In our study, Sediment-QMCI, %Decreaser abundance and %Decreaser richness gen-
erally performed better than their widely used counterparts in terms of the strength of the
relationships with deposited fine sediment detected. However, the detection frequency of
meaningful relationships for all sediment-specific invertebrate metrics was not higher
than for their counterparts. Consequently, while these new metrics are promising, it
appears they are not yet able to consistently detect sediment impacts where their more
general counterparts cannot. Therefore, we suggest that future sediment-related
studies in New Zealand should also include these metrics in their analyses, to enable
the metrics to continually evolve and improve.
Acknowledgements
We thank the team from Environment Southland for undertaking the data collection and Nuwan
DeSilva for his helpful correspondence through the writing process.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Roger Hodson http://orcid.org/0000-0002-4659-4487
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