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Water physicochemistry and benthic macroinvertebrate communities in a tropical reservoir: The role of water level fluctuations and water depth
Accepted Manuscript Title: Water physicochemistry and benthic macroinvertebrate communities in a tropical reservoir: the role of water level fluctuations and water depth Author: Francis S. Magbanua Nikki Yvette B. Mendoza Christine Jewel C. Uy Christoph D. Matthaei Perry S. Ong PII: DOI: Reference:
S0075-9511(15)00075-4 http://dx.doi.org/doi:10.1016/j.limno.2015.10.002 LIMNO 25475
To appear in: Received date: Revised date: Accepted date:
1-9-2014 31-8-2015 19-10-2015
Please cite this article as: Magbanua, F.S., Mendoza, N.Y.B., Uy, C.J.C., Matthaei, C.D., Ong, P.S.,Water physicochemistry and benthic macroinvertebrate communities in a tropical reservoir: the role of water level fluctuations and water depth, Limnologica (2015), http://dx.doi.org/10.1016/j.limno.2015.10.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Water physicochemistry and benthic macroinvertebrate communities in a tropical reservoir: the role of water level fluctuations and water depth
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Christoph D. Matthaeib and Perry S. Onga
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Francis S. Magbanuaa,* , Nikki Yvette B. Mendozaa, Christine Jewel C. Uya,1,
Institute of Biology, University of the Philippines Diliman, Quezon City 1101, Philippines Department of Zoology, University of Otago, Dunedin 9054, New Zealand
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Present address: Division of Life Sciences, College of Life Sciences and Biotechnology,
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Korea University, Seoul, Korea *
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Corresponding author: Francis S. Magbanua. E-mail: [email protected],
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Telefax +63 2 920 5471
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Revised version resubmitted to Limnologica; manuscript type: Research paper
Running title: Effects of water level fluctuation and depth
Key words: Philippines; Pantabangan Reservoir; littoral; water-level management; drawdown; climate change
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Abstract
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Water level fluctuations due to reservoir operations often cause spatial and temporal
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differences in water chemistry, which in turn can have considerable biological effects.
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Nonetheless, few studies have investigated the effects of fluctuating water levels on water
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quality and benthic macroinvertebrates in reservoirs in tropical countries, and none in the
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Philippine archipelago. We investigated the littoral zone of a Philippine reservoir subject to
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strong water level fluctuations and determined whether (i) water quality and
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macroinvertebrate community health is reduced when water levels are low, (ii) water quality
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declines with increasing water depth regardless of the overall water level, and (iii) water
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quality and community health decrease more strongly with water depth during low water
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level periods. Our study included five sites and four depths at each site, with three collections
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each during high and low water levels. Low water levels may have negatively affected four
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water quality and 10 biological metrics, whereas depth may have negatively affected two
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water quality and five biological metrics. Significant overall water level by depth interactions
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were detected for four common taxa but none for water physicochemistry. Our findings show
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that tropical reservoirs may experience reduced water quality at low water levels, which can
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affect their biodiversity and potentially their ecological functioning.
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Introduction
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In many lakes and reservoirs worldwide, large fluctuations in water levels have been shown
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to adversely affect littoral geomorphology (Furey et al., 2004; Hofmann et al., 2008), water
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chemistry (Dinka et al., 2004; Zhu et al., 2013) and biodiversity (Zohary and Ostrovsky,
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2011; Sutela et al., 2013). The most drastic effects often occur in hydroelectric reservoirs
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where the drawdown is large and changes in water level are frequent (Smith et al., 1987).
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Moreover, climate change is expected to further magnify the seasonal and annual amplitudes
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of water-level fluctuation due to increased occurrence of extreme floods and droughts
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(Abrahams, 2008; Zohary and Ostrovsky, 2011). Most of the studies on the impacts of water
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level fluctuations in lakes or reservoirs have been conducted in temperate regions (see
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reviews by Leira and Cantonati, 2008; Gantzer et al., 2009), with few studies in the tropics
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(e.g. Dalu et al., 2012; Kolding and van Zwieten, 2012) and none in the Philippine
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archipelago.
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Water-level fluctuations are more frequent in lakes and reservoirs in regions where rain events are strongly seasonal and occur in an irregular precipitation regime (Geraldes and
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Boavida, 2005). Tropical lakes and reservoirs fluctuate seasonally between maximum levels
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during or towards the end of the rainy season and minimum levels at the end of the dry
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season (Dalu et al., 2012; Peng et al., 2012). Such fluctuations often cause spatial and
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temporal differences in water chemistry, which in turn can affect the diversity, density and
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overall resilience of reservoir biota (McEwen and Butler, 2010).
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Amongst freshwater biota, benthic macroinvertebrates are the most commonly used
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organisms for the assessment and monitoring of the ecological status of lakes and reservoirs
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(Bailey et al., 2001). One reason for their popularity as bioindicators is that benthic
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macroinvertebrates play an important role in the coupling of benthic and pelagic food webs
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and in organic matter cycling and energy flow (Liu et al., 2012). Furthermore, they often
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show the strongest responses among bioindicators to water drawdown (Sutela et al., 2013;
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White et al., 2008). For instance, a marked decline in littoral macroinvertebrate taxon
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richness and density with increasing amplitude in water level fluctuation has been observed
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in Finnish lakes (Aroviita and Hämäläinen, 2008) and in a reservoir in Minnesota, U.S.A
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(McEwen and Butler, 2010). The former study compared 11 lakes with different regulation
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amplitudes to 12 unregulated lakes by sampling macroinvertebrates in the upper and lower
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littoral zones, and the authors suggested that freezing and flushing of sediments (and possibly
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nutrients) in late winter led to an impoverished upper littoral macroinvertebrate fauna in the
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regulated lakes. The latter study assessed changes in littoral macroinvertebrate assemblages
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using a before-after control-impact design with an unregulated lake as a control system
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before and after implementation of a new water level regime in a reservoir. Reduced
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macroinvertebrate densities at 1-2 m depths, driven by losses of amphipods and chironomids,
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were observed in the reservoir relative to the control lake.
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Composition and abundance of benthic macroinvertebrates in lakes and reservoirs
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typically vary considerably with water depth, with species richness generally being higher
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near the shore than in deep waters (Strayer, 2009; Ngupula and Kayanda, 2010). Baumgärtner
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et al. (2008) observed a gradual transition in macroinvertebrate community structure with
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water depth, with pronounced similarities within a depth zone and marked differences
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between adjacent zones. These authors suggested that availability of food resources may
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explain the observed patterns because water depth affects temperature, oxygen concentration
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and light availability which, in turn, determine photosynthetic rates and thus food availability.
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In some deep Mediterranean lakes, intense water use for irrigation coupled with
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summer drought has resulted in reduced lake volumes and increased eutrophication (Wantzen
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et al., 2008). Indeed, hypolimnion thickness can be markedly related to lake water level, with
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hypolimnic concentrations of nutrients (total phosphorus and ammonium) and hydrogen
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sulphide increasing as water level declines (Zohary and Ostrovsky, 2011). These reductions
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in water quality may lead to changes in macroinvertebrate communities that can influence
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energy flow and nutrient cycling (Hansen et al., 1998; Rachamim et al., 2010), with pervasive
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effects on lake ecosystem health.
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In the Philippines, there has been no research on the impact of fluctuating water levels
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on the littoral benthic macroinvertebrate communities of lakes and reservoirs, in spite of the
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fact that such water-level manipulations are common. In the main island of Luzon there are
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seven major reservoirs, including the Pantabangan Reservoir in the province of Nueva Ecija.
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The Pantabangan Reservoir is the country’s largest reservoir. It can store up to three billion
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cubic meters of water, irrigates more than 100,000 hectares of rice lands in Central Luzon
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and generates 132 megawatts of hydroelectric power (Philippine National Power
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Corporation, 2010). Marked water level fluctuations occur because of these uses, especially
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during intense water use for irrigation during the dry season. The annual water-level
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fluctuation amplitude (maximum minus minimum water levels) ranges from 11.1 m to 41.5
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m, with an average of 23.8 (±1.2) m over the last 30 years. A recent study of precipitation
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patterns in the Pantabanagan-Carranglan catchment (Lasco et al., 2010) revealed an
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increasing frequency in the occurrence of droughts associated with El Niño episodes over the
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last 20 years. The same authors also reported that the timing of the onset of the rainy season
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has become more variable since 2000. These changes in climatic patterns are expected to
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increase the magnitude of the water level fluctuations occurring in the Pantabangan
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Reservoir.
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Against this background, our main objective was to examine how water level fluctuations in the Pantabangan Reservoir affect physicochemical water quality and the
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benthic macroinvertebrate community. At a more general level, this work also represents one
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of the first scientific investigations of the benthic macroinvertebrate community in the littoral
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zone of a reservoir in the Philippines. Based on the related research cited above, we predicted
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that (i) physicochemical water quality and benthic macroinvertebrate community health will
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be lower when water levels in the reservoir are low, (ii) water quality will decline with
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increasing water depth, regardless of the overall water level in the reservoir, and (iii) water
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quality and benthic macroinvertebrate community health will decrease more strongly with
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water depth when water levels in the reservoir are low.
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Methods
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Study site
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The Pantabangan Reservoir (15o 48’ 52” N, 121o 06’ 29” E) is situated at 230 m a.s.l., has a
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surface area of 8,900 ha and a maximum depth of 28.9 m (Guerrero, 1988), and its surface
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waters are eutrophic (mean total P concentration measured 19 times between 2012 and 2014
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= 102.3 ± 5.6 µg/L; F.S. Magbanua, unpublished data). The catchment supplying water to the
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reservoir covers 853 km2 and is located in the townships of Pantabangan and Carranglan
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(Nueva Ecija province), Alfonso Castañeda and Dupax del Sur (Nueva Vizcaya), and Maria
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Aurora (Aurora) (Lasco et al., 2010). Agriculture and fisheries are the main sources of
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livelihood in the catchment. Slash-and-burn farming and charcoal making are still practiced
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by the majority of the residents in the catchment.
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The catchment has a climate with two pronounced seasons: dry from November to April and wet during the rest of the year. The average monthly rainfall from 1980-2010 in the
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town of Pantabangan was 162.2 (± 42.1, standard error) mm, with the highest monthly
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averages (> 300 mm) from July to September and the least rainfall (< 16 mm per month)
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from January to March (Fig. 1A). The average water level in the reservoir during the same
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period was 193.3 (± 2.2) m, and variations in water levels were seasonal and related to
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monthly precipitation. The reservoir’s annual water level cycle is characterized by four
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different phases, each with a duration of three months. The maximal water level phase
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(hereafter called ‘high water level’) lasts from October to December, the emptying phase
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from January to March, the minimal level phase (hereafter called ‘low water level’) from
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April to June, and the refilling phase from July to September (Fig. 1A).
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Fig. 1. Mean rainfall in the catchment and water level in Pantabangan Reservoir for the
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period 1980-2010 (A) and for the study year, 2012 (B). Data are from the Philippine National
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Irrigation Administration – Upper Pampanga River Integrated Irrigation Systems (NIA–
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UPRIIS).
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In our study, we sampled the reservoir on three dates each during the low water level phase (18-19 May, 13-14 June and 6-7 July 2012) and the high water level phase (26-27
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October, 23-24 November and 8-9 December 2012) (Fig. 1B). Five sites were selected in the
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reservoir: one in the lacustrine zone (P1; stagnant water; near the dam), one in the
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intermediate zone (P2), and three in the riverine zones (slowly flowing water) of the main
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tributaries located in the northern (Carranglan River, P3; Diamman River, P4) and southern
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(Digoliat and San Juan rivers, P5) basins of the reservoir (Fig. 2). At each site, a single
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transect was drawn perpendicular to the coastline. Along this transect, four water depths were
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sampled (1, 5, 10 and 15 m) using a boat. The sampling depth was determined by lowering a
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weighted line from the boat until it touched the bottom of the reservoir. Because the average
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water level of the reservoir in 2012 increased from 206.6 (± 2.8) m in May-July to 212.7 (±
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0.1) m in October-December, water depth (surface to bottom) at a given set of coordinates in
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a given transect was not constant during our return visits. Thus, in terms of their positions
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along the sampling transect at each site, the 1-m and 5-m depth sampling points during the
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low water level period corresponded approximately to the 10-n and 15-m depth sampling
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points during the high water level period. For the study year, 2012, the average water-level
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fluctuation amplitude (maximum minus minimum water levels, determined monthly) was
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35.1 (± 18.2) m, a value somewhat higher than the mean long-term annual fluctuation
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amplitude (23.8 m; see above).
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Fig. 2. Map of Pantabangan Reservoir showing the locations of the five sampling sites.
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Water physicochemistry
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Dissolved oxygen (DO), temperature (°C), pH, conductivity, and total dissolved solids (TDS)
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were measured onsite at total water depths (surface to bottom) of 1, 5, 10 and 15 m using a
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multi-parameter water quality meter (YSI Professional Plus; Yellow Spring Instruments,
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Ohio, USA). At each site, these parameters were measured along the single transect at the
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above four water depths, and the four sampling points were always 20-100 m apart. At the 5-
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m depth sampling point, water samples were collected both at 1 m and at 5 m below the
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surface. Similarly, at the 10-m and 15-m depth sampling points, water samples were collected
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at 1, 5 and 10 m below the surface and at 1, 5, 10 and 15 m below the surface, respectively.
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Water transparency (in m) was also determined with a 20 cm-diameter, black-and-white
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Secchi disk submerged below the surface. Moreover, three 500-mL water samples for total
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nitrogen (TN) and total phosphorus (TP) analyses were collected using a Van Dorn-style
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sampling bottle at each water depth mentioned above. All measurements were conducted
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between 9:00 am and 2:00 pm. These water samples were transported to the laboratory in a
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container covered with ice and stored at -20oC until analysis using standard, colorimetric
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protocols (APHA, 1999).
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Benthic macroinvertebrates
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At each sampling site and depth, three benthic macroinvertebrate samples (distance between
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samples about 5 m) were collected using an Ekman dredge (surface area 60 x 35 cm). A total
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of 360 samples were collected: 3 samples x 5 sites x 4 water depths x 6 sampling dates (3
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during the low water level period and 3 during the high water level period). All materials
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including sediments collected in the dredge were emptied to a 12-L sieve bucket (600- µm
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mesh, WaterMark® sieve bucket, Forestry Suppliers, Inc., Jackson, MA). Filtered materials
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were preserved in 90% ethanol in the field. In the laboratory, samples were further washed
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and elutriated using a 250-µm sieve to separate macroinvertebrates from plants and inorganic
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materials. Macroinvertebrates were identified and counted under a stereo microscope (Zeiss
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STEMI 2000c, magnification 6.5-50x; Carl Zeiss, Göttingen, Germany). Identification was
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done to the lowest feasible taxonomic level using keys of Dudgeon (1999) and Yule and
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Yong (2004). Invertebrate numbers per sample were extrapolated to densities per square m.
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Prior to data analysis, the values from the samples collected on the three dates within each water level period were averaged, resulting in 60 data points per water level period.
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Data analysis
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Where necessary, data were log10 (x) or log10 (x + 1) transformed to improve normality and
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homoscedasticity after exploratory data analysis. All analyses (repeated-measures, nested
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two-way (M)ANOVAS with block factor) were conducted in IBM SPSS Statistics 20.0 (IBM
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Corp., New York, USA). In this model, sampling depth (4 levels) was a fixed main (between-
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subjects effects) factor while water level (low vs high) was a repeated-measures (within-
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subjects contrasts) factor. In addition, sampling location (the five sites) was a block factor
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and sample number (1-3) a nested factor (nested within sampling depth).
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For macroinvertebrate community-level metrics (macroinvertebrate density, taxon
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richness, Simpson’s diversity and evenness) and most water physicochemistry variables
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(temperature, DO, conductivity, TDS, TN and TP), the ANOVA model was intercept (d.f. 1)
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+ depth (3) + sample (depth) (8) + block [4] + error [44; n = 60] for tests of between-subjects
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effects and time (d.f. 1) + time x depth (3) + time x sample (depth) (8) + time x block [4] +
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error (time) [44] for tests of within-subjects effects. For water transparency, all values
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determined above the 1-m depth sampling locations were zero (due to uniformly high
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turbidity at these shallow locations). Therefore, only the data collected above the 5, 10 and
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15 m sampling locations were analyzed for this variable.
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If between-subjects effects were significant, pairwise comparisons was performed for the factor depth using post hoc tests (Tukey’s HSD). To determine changes in community
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composition, the densities of the eight most common invertebrate taxa were analyzed with the
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multivariate equivalent of the ANOVA model above. All presented results were significant
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(P < 0.05). Results for the block factor (sampling site) are not shown because any significant
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effects merely represent background variation that is unimportant for our research objectives.
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Results
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Water quality
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Except for DO and TN, all water quality variables measured differed markedly between water
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levels (Table 1). Water transparency, pH, TDS and TP were highest during the high water
218
level period whereas temperature and conductivity were highest during the low water level
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period. Water temperature, DO and TN also differed across sampling depths (Table 1).
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Temperatures at 1 m or 5 m water depth (range 26.7–31.5oC) were warmer than at 10 m or 15
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m (range 24.4–30.3oC). DO was higher at 1 m than at 15 m depth, whereas TN showed the
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opposite pattern. None of the water quality variables showed an interaction between water
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level and sampling depth (Table 1).
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Table 1. Summary (means ± SE and P-values) of the ANOVAs comparing water quality variables between water level periods (within-subjects factor) and
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sampling depths (between-subjects factor). N = 60, except for water transparency where n = 45. DO – dissolved oxygen, TDS – total dissolved solids, TN –
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total nitrogen, TP – total phosphorus. Rankings for specific contrasts or post hoc tests in cases with significant effects are given. P-values < 0.05 are in bold
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print. Water level
Response variable
P-
M
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Low
High
1.6 (0.1)
2.2 (0.2)
<0.001
Temp. (0C)
29.6 (0.1)
27.2 (0.1)
<0.001
DO (mg L-1)
6.6 (0.9)
5.7 (0.1)
0.927
pH
8.7 (0.1)
9.7 (0.2)
<0.001
132.8 (2.4)
128.7 (1.2)
(µS cm-1)
Ac
Conductivity
Ranking
P-value
5m
10 m
15 m
High > Low
-
2.4 (0.2)
2.6 (0.1)
2.5 (0.1)
0.465
Low > High
28.8 (0.3)
28.7 (0.3)
28.2 (0.3)
27.9 (0.3)
<0.001
(1 = 5) > (10 = 15)
0.731
8.0 (1.8)
5.9 (0.2)
5.8 (0.3)
5.0 (0.3)
0.002
1 > 15
0.539
High > Low
9.0 (0.2)
9.0 (0.2)
8.7 (0.3)
8.8 (0.3)
0.395
0.400
<0.001
Low > High
127.4 (4.5)
133.4 (2.4)
130.7 (0.9)
131.5 (1.3)
0.575
0.402
High > Low
0.07 (0.004)
0.07 (0.004)
0.09 (0.02)
0.07 (0.003)
0.385
0.452
8.9 (1.3)
10.2 (1.2)
11.9 (1.3)
12.8 (1.2)
0.002
0.18 (0.05)
0.34 (0.11)
0.40 (0.13)
0.30 (0.09)
0.341
ed
pt
parency (m)
ce
Water trans-
P-
1m
value
Contrast
Interaction
Sampling depth
TDS (mg L-1)
0.05 (0.003)
0.09 (0.01)
<0.001
TN (mg L-1)
10.7 (0.7)
11.1 (1.0)
0.568
TP (mg L-1)
0.01 (0.001)
0.60 (0.09)
<0.001
High > Low
value
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0.623
(10 = 15) > 1
0.102 0.367
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Table 2. Summary (means ± SE and P-values) of the (M)ANOVAs comparing invertebrate responses (n=60) between water level periods (within-subjects
239
factor) and sampling depths (between-subjects factor). Rankings for specific contrasts or post hoc tests in cases with significant effects are given. P-values <
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0.05 are in bold print. Response variable
Water level PHigh value
89 (24)
576 (69)
<0.001
Tanypodinae
36 (7)
163 (34)
<0.001
Bezzia spp.
1 (1)
88 (15)
<0.001
Caenodes spp.
1 (1)
34 (9)
<0.001
pt
ce
23 (5)
<0.001
0 (0)
16 (4)
<0.001
Ac
Glossosoma spp.
0 (0)
Corbicula fluminea
19 (7)
0 (0)
<0.001
Tarebia granifera
9 (4)
1 (1)
0.041
Community-level metrics Invertebrate density 166 (29.2) Taxon richness Simpson’s diversity
Interaction
1m
5m
10 m
15 m
P-value
Ranking
P-value
High > Low
451 (119)
455 (79)
295 (72)
129 (39)
0.043
5 > 15
0.141
High > Low
46 (16)
207 (63)
97 (25)
48 (12)
<0.001
5 > 15 > 1; 10 > 1
0.331
High > Low
10 (4)
66 (20)
73 (23)
29 (8)
<0.001
(5 = 10 = 15) > 1
0.001
High > Low
16 (9)
29 (10)
4 (2)
20 (13)
0.013
5 > (10 = 15)
0.049
High > Low
11 (6)
22 (7)
11 (4)
1 (1)
0.010
5 > 15
0.010
High > Low
1 (1)
6 (3)
11 (5)
13 (5)
0.146
Low > High
3 (2)
3 (3)
27 (14)
6 (3)
0.036
10 > 5
0.036
Low > High
10 (7)
10 (4)
0 (0)
0 (0)
0.023
5 > (10 = 15)
0.221
ed
Chironominae
Sampling depth
Contrast
M
Low
Community composition
Ecnomus spp.
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0.146
918 (96.8)
<0.001
High > Low
557 (144)
820 (147)
533 (99)
257 (62)
0.014
5 > 15
0.802
1 (0.2)
4 (0.2)
<0.001
High > Low
2 (0.3)
4 (0.4)
3 (0.3)
2 (0.3)
<0.001
5 > 15 > 1; 10 > 1
0.937
1.2 (0.2)
2.7 (0.2)
<0.001
High > Low
1.1 (0.2)
2.0 (0.2)
2.4 (0.3)
2.4 (0.4)
0.002
(5 = 10 = 15) > 1
0.651
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i cr 0.8 (0.1)
0.001
High > Low
us
0.5 (0.1)
0.6 (0.1)
0.6 (0.1)
0.7 (0.1)
0.8 (0.1)
0.304
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Simpson’s evenness
Ac
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pt
ed
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0.162
Benthic macroinvertebrates
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A total of 1508 individuals belonging to 20 taxa were recorded in the 360 samples. Of these,
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eight comprised 91.2% of the total: the two midge subfamilies Chironominae (28.6%) and
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Tanypodinae (23.3%), the ceratopogonid Bezzia spp. (14.2%), the mayfly Caenodes spp.
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(7.2%), the caddis flies Ecnomus spp. (6.6%) and Glossosoma spp. (5.0%), the Asian clam
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Corbicula fluminea (4.1%) and the Asian prosobranch snail Tarebia granifera (2.2%). The
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MANOVA based on these eight common taxa showed significant overall effects of water
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level (Wilk’s λ P < 0.001), sampling depth (Wilk’s λ P < 0.001) and an overall interaction of
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the two factors (Wilk’s λ P = 0.002). Furthermore, the between-subjects effects in the
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MANOVA revealed that the overall effect of water level was due to effects of water level on
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all eight individual taxa (Table 2). Of these, only C. fluminea and T. granifera had a higher
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density during the low water level period, while the other six taxa showed higher densities
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during the high water level period. The significant overall effect of sampling depth was due
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to the density patterns of seven taxa (Chironomidae, Tanypodinae, Bezzia, Caenodes,
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Ecnomus, C. fluminea and T. granifera); except for C. fluminea, all these displayed their
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highest densities at 5 m depth (Table 2). In addition, Bezzia, Caenodes, Ecnomus and C.
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fluminea displayed interactions of water level and sampling depth. During the low water level
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period, densities of the first three taxa were uniformly low or absent across the four sampling
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depths. During the high water level period, by contrast, densities of the same three taxa were
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much higher and also varied considerably across sampling depths (Fig. 3), with density
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maxima always occurring either at 5 m or 10 m. For C. fluminea, the shape of the interaction
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was different: this taxon was completely absent at high water level, whereas its densities
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varied across sampling depth at low water level, peaking at 10 m depth (Fig. 3).
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Fig. 3. Density patterns for common benthic invertebrate taxa due to the interaction of water
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level (low: ●; high: ■) and sampling depth in Pantabangan Reservoir.
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While none of the community-level macroinvertebrate metrics showed a significant
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interaction effect, values for all four metrics were higher during the high water level period
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and all metrics except for evenness differed across sampling depths (Table 2). Both total
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macroinvertebrate density and taxon richness were highest at 5 m depth, whereas Simpson’s
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diversity was highest at 10 and 15 m.
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Discussion
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High versus low water level periods
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Our prediction that both physicochemical water quality and benthic macroinvertebrate
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community health should generally be lower when water levels in the Pantabangan Reservoir
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are low was largely supported by our findings, especially for the macroinvertebrates. Ten of
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the 12 studied individual macroinvertebrate response variables showed significantly lower
284
values. Water temperature and conductivity were also higher during the low water level
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period, presumably due to increased air temperatures and reduced water inflow volumes.
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Several earlier studies in reservoirs in both tropical and subtropical climates have shown
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inverse relationships between lake/reservoir water level and water temperature (Dejenie et al.,
288
2008; Zhu et al., 2013), and also between water level and conductivity (Dejenie et al., 2008;
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Jose de Paggi and Devercelli, 2011). In their study of the environmental factors influencing
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macroinvertebrate communities in the littoral zone of a reservoir in Zimbabwe, Dalu et al.
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(2012) attributed the increase in conductivity during low water level periods to evaporation-
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driven concentration of dissolved substances in the water.
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A similar pattern of decreases in macroinvertebrate taxon richness, density and
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abundance as in our study has been observed in another tropical reservoir in China due to
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increased water level fluctuations (Zhang et al., 2012). As pointed out in the Introduction,
296
limited studies have been done in tropical and subtropical lakes and reservoirs on the effects
Page 18 of 29
of changing water levels on macroinvertebrate communities compared to their temperate
298
counterparts. Most of the research in the tropics and subtropics that directly examined the
299
effects of water drawdown focussed on macrophyte (e.g. Boschilia et al., 2012) or plankton
300
(e.g. Okogwu and Ugwumba, 2012) assemblages.
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Water quality and water depth
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Our hypothesis that water quality should decline with increasing sampling depth, regardless
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of the overall water level in the reservoir, was partly supported. Only three of the eight
305
studied physicochemical metrics showed the predicted patterns: water temperature and
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dissolved oxygen readings were highest at the two shallowest water depths while total
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nitrogen concentration was highest at the greatest depth sampled. While we did not measure
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actual lake stratification in Pantabangan Reservoir, we observed an increasing pattern of total
309
nitrogen concentrations in parallel with water depth. Nonetheless, nutrient inputs from the
310
surrounding catchment (e.g. dissolved nutrients entering the reservoir via tributary inflows
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and surface runoff from fertilized pasture and croplands) may have contributed in the
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increased nutrient concentrations in the reservoir.
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Thermal stratification is typical for many standing waters, including Philippine lakes,
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due to the warming of surface water by the sun while the deeper water layers remain colder
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by comparison (Santiago and Arcilla, 1993). Moreover, the dissolved oxygen pattern we
316
observed was consistent with the clinograde curve typical for eutrophic lakes (Wetzel, 2001)
317
and reservoirs (e.g. Araújo et al., 2011). This pattern has also been observed in Lake
318
Sampaloc (Santiago and Arcilla, 1993), a natural lake located in the same region of the
319
Philippines as Pantabangan Reservoir.
320 321
Vertical mixing and turnover are important processes in the nutrient cycling in many deep lakes and reservoirs (Boehrer and Schultze, 2008). However, this vertical nutrient
Page 19 of 29
exchange is relatively weak in tropical and subtropical regions due to warm climates, in
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contrast to temperate climates where inverse stratification is common (Peng et al., 2012).
324
Similarly, Lewis (2000) reported that deep mixing happens only occasionally in tropical lakes
325
and reservoirs because wind-driven surface waves are rarely strong enough to mix epilimnion
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and hypolimnion completely. Nonetheless, river inflows and precipitation in the catchment
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play a critical role in causing changes in mean water depth and nutrient inputs in tropical and
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subtropical reservoirs (Lewis, 2000; Peng et al., 2012).
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Interactive effects of water depth and water level period
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Our final hypothesis predicted that water quality and benthic macroinvertebrate community
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health should decrease more strongly with water depth when water levels in the reservoir are
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generally low. This hypothesis received no support for water physicochemistry but was
334
unequivocally supported for macroinvertebrate community health. These contrasting findings
335
suggest that other, unmeasured factors may be influencing the macroinvertebrate community.
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Of the eight investigated water physicochemistry variables, only temperature showed
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a significant interaction between water level and sampling depth, and the observed interaction
338
pattern for temperature cannot be interpreted readily as a reduction in water quality as other
339
factors may be involved. Lewis (2000) noted that at high temperatures, the ability of water in
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tropical lakes to hold oxygen decreases and that oxygen is further removed more rapidly at
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the hypolimnion because of high rates of microbial metabolism at high temperatures.
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Of the macroinvertebrate variables, none of the four individual community-level
343
metrics displayed overall significant interactions, but macroinvertebrate community
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composition determined with the MANOVA did. This overall interaction was due to the
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distributions of four of the eight most common macroinvertebrate taxa (Bezzia, Caenodes,
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Ecnomus and C. fluminea), the first three of which all showed a similar pattern. While this
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pattern (low water level: uniformly low abundance or absent across all four depths; high
348
water level: more abundant, with density maximum at 5m or 10m depth) did not quite follow
349
the shape we had predicted, it can still be interpreted as indicating that macroinvertebrate
350
community health declined more markedly (in relative terms) with sampling depth when
351
water level in the reservoir was low than when water level was high. These findings contrast
352
with those from two reservoirs in subtropical China (Hu et al., 2012; Liu et al., 2012;
353
although both studies did not directly examine the effects of water level fluctuations) where
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macroinvertebrate density generally declined with water depth. The former authors identified
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water depth as the only physicochemical variable with the potential to explain this density
356
pattern, while the latter authors suggested this decline could be due to thermal stratification.
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Seasonal variations in physicochemistry and the potential role of total phosphorus
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Generally, most environmental parameters exhibit seasonal variations (Araújo et al., 2011;
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Dalu et al., 2012; Zhang et al., 2014), and these variations may influence the distribution and
361
abundance of macroinvertebrate communities. Our study found differences in several water
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physicochemistry parameters in parallel with water level fluctuations. We acknowledge that
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these differences may also be related to seasonal variation (as discussed earlier on). However,
364
total nitrogen and dissolved oxygen concentrations showed no significant seasonal changes in
365
our study, consistent with the findings of Zhang et al. (2014) for the Xinlicheng Reservoir in
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China (for total nitrogen) and of Oliveira et al. (2014) for the Apipucos Reservoir in
367
Pernambuco, Brazil (for both total nitrogen and dissolved oxygen). Moreover, in a recent
368
study of three tropical reservoirs in the Paraopeba River catchment in southern Brazil,
369
patterns in the macroinvertebrate community were unrelated to seasonal changes in
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temperature and rainfall (Molozzi et al., 2013). Further, total phosphorus concentration in the
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Funil Reservoir in Southern Brazil was higher during the wet season, presumably due to
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increased inflow of nutrient-rich water from the river feeding the reservoir (Araújo et al.,
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2011). By contrast, in our study total phosphorus concentration was higher during the dry
374
season, possibly because a larger area of reservoir sediment was inundated (perhaps leading
375
to nutrient resuspension) as this season coincided with relatively higher water level in the
376
reservoir. The relatively low concentration during the wet season may have been due to
377
higher water inflows resulting in increased flushing thus improving the reservoir’s water
378
quality. Consequently, there is some circumstantial evidence that total phosphorus
379
concentration may have played a role in creating the macroinvertebrate responses we
380
observed. Nonetheless, we cannot reliably identify the observed difference in total
381
phosphorus as a cause for the variations that we detected in the macroinvertebrate
382
community. This is due to the observational nature of our study and because most of the
383
widely used macroinvertebrate taxonomic metrics, community descriptors and indices in
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standing waters including river-reservoir systems are generally poorly correlated with total
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phosphorus (e.g. McGoff et al., 2013), pH, total dissolved solids and conductivity (e.g.
386
Yazdian et al., 2014).
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Conclusions
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Our study, albeit observational in nature, is one of relatively few existing assessments of the
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potential effects of water-level fluctuations on benthic macroinvertebrate communities in
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standing freshwaters in the tropics. We recognize that our study is unreplicated at the
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reservoir scale and also lacks a suitable control (an unregulated lake) for comparison.
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Nevertheless the study represents a significant gain in knowledge, especially for the
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Philippines, because there has been no previous research on this topic in Philippine lakes and
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reservoirs.
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Regarding the Pantabangan Reservoir, hopefully the data presented in our paper will be complemented in the future by regular monitoring of water quality and benthic
398
macroinvertebrate community health. Investigations in the major tributaries draining into the
399
reservoir are now underway, to determine the influence of river flows, dissolved and
400
suspended solids from soil erosion and possible water quality degradation because of
401
agricultural activities in the catchment. However, it is also important to investigate the
402
general limnology of the reservoir because currently much of the basic information is lacking,
403
including stratification regime, bathymetry, and annual dynamics of the key chemical
404
parameters at different depths along the water column. In the longer term, such information
405
will be able to guide resource managers and local authorities in the development of a science-
406
based catchment-scale restoration program. This program should include a management
407
strategy to limit or reduce inputs of nutrients, fine sediment and other pollutants in order to
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protect and enhance freshwater biodiversity in the reservoir.
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Acknowledgments
We thank the First Gen Hydro Power Corporation, Diliman Science Research Foundation and
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the Institute of Biology, University of the Philippines Diliman for financial and logistical
413
support. We also acknowledge the National Irrigation Administration – Upper Pampanga
414
River Integrated Irrigation Systems (NIA–UPRIIS) for providing the 1980 to 2012 water
415
level and rainfall data for the Pantabangan Reservoir. Special thanks to our colleagues from
416
the First Gen Hydro Power Corporation and the Institute of Biology for their help in the field,
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Jovilyn Fernandez and Paolo Garrido for their help with Figure 2, and Daniel Hering and an
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anonymous reviewer for helpful comments on the manuscript.
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S0075-9511(15)00075-4 http://dx.doi.org/doi:10.1016/j.limno.2015.10.002 LIMNO 25475
To appear in: Received date: Revised date: Accepted date:
1-9-2014 31-8-2015 19-10-2015
Please cite this article as: Magbanua, F.S., Mendoza, N.Y.B., Uy, C.J.C., Matthaei, C.D., Ong, P.S.,Water physicochemistry and benthic macroinvertebrate communities in a tropical reservoir: the role of water level fluctuations and water depth, Limnologica (2015), http://dx.doi.org/10.1016/j.limno.2015.10.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Water physicochemistry and benthic macroinvertebrate communities in a tropical reservoir: the role of water level fluctuations and water depth
a
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Christoph D. Matthaeib and Perry S. Onga
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Francis S. Magbanuaa,* , Nikki Yvette B. Mendozaa, Christine Jewel C. Uya,1,
Institute of Biology, University of the Philippines Diliman, Quezon City 1101, Philippines Department of Zoology, University of Otago, Dunedin 9054, New Zealand
1
Present address: Division of Life Sciences, College of Life Sciences and Biotechnology,
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b
Korea University, Seoul, Korea *
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Corresponding author: Francis S. Magbanua. E-mail: [email protected],
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Telefax +63 2 920 5471
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Revised version resubmitted to Limnologica; manuscript type: Research paper
Running title: Effects of water level fluctuation and depth
Key words: Philippines; Pantabangan Reservoir; littoral; water-level management; drawdown; climate change
Page 1 of 29
Abstract
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Water level fluctuations due to reservoir operations often cause spatial and temporal
3
differences in water chemistry, which in turn can have considerable biological effects.
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Nonetheless, few studies have investigated the effects of fluctuating water levels on water
5
quality and benthic macroinvertebrates in reservoirs in tropical countries, and none in the
6
Philippine archipelago. We investigated the littoral zone of a Philippine reservoir subject to
7
strong water level fluctuations and determined whether (i) water quality and
8
macroinvertebrate community health is reduced when water levels are low, (ii) water quality
9
declines with increasing water depth regardless of the overall water level, and (iii) water
10
quality and community health decrease more strongly with water depth during low water
11
level periods. Our study included five sites and four depths at each site, with three collections
12
each during high and low water levels. Low water levels may have negatively affected four
13
water quality and 10 biological metrics, whereas depth may have negatively affected two
14
water quality and five biological metrics. Significant overall water level by depth interactions
15
were detected for four common taxa but none for water physicochemistry. Our findings show
16
that tropical reservoirs may experience reduced water quality at low water levels, which can
17
affect their biodiversity and potentially their ecological functioning.
19
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Introduction
20
In many lakes and reservoirs worldwide, large fluctuations in water levels have been shown
21
to adversely affect littoral geomorphology (Furey et al., 2004; Hofmann et al., 2008), water
22
chemistry (Dinka et al., 2004; Zhu et al., 2013) and biodiversity (Zohary and Ostrovsky,
23
2011; Sutela et al., 2013). The most drastic effects often occur in hydroelectric reservoirs
24
where the drawdown is large and changes in water level are frequent (Smith et al., 1987).
25
Moreover, climate change is expected to further magnify the seasonal and annual amplitudes
Page 2 of 29
of water-level fluctuation due to increased occurrence of extreme floods and droughts
27
(Abrahams, 2008; Zohary and Ostrovsky, 2011). Most of the studies on the impacts of water
28
level fluctuations in lakes or reservoirs have been conducted in temperate regions (see
29
reviews by Leira and Cantonati, 2008; Gantzer et al., 2009), with few studies in the tropics
30
(e.g. Dalu et al., 2012; Kolding and van Zwieten, 2012) and none in the Philippine
31
archipelago.
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Water-level fluctuations are more frequent in lakes and reservoirs in regions where rain events are strongly seasonal and occur in an irregular precipitation regime (Geraldes and
34
Boavida, 2005). Tropical lakes and reservoirs fluctuate seasonally between maximum levels
35
during or towards the end of the rainy season and minimum levels at the end of the dry
36
season (Dalu et al., 2012; Peng et al., 2012). Such fluctuations often cause spatial and
37
temporal differences in water chemistry, which in turn can affect the diversity, density and
38
overall resilience of reservoir biota (McEwen and Butler, 2010).
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Amongst freshwater biota, benthic macroinvertebrates are the most commonly used
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organisms for the assessment and monitoring of the ecological status of lakes and reservoirs
41
(Bailey et al., 2001). One reason for their popularity as bioindicators is that benthic
42
macroinvertebrates play an important role in the coupling of benthic and pelagic food webs
43
and in organic matter cycling and energy flow (Liu et al., 2012). Furthermore, they often
44
show the strongest responses among bioindicators to water drawdown (Sutela et al., 2013;
45
White et al., 2008). For instance, a marked decline in littoral macroinvertebrate taxon
46
richness and density with increasing amplitude in water level fluctuation has been observed
47
in Finnish lakes (Aroviita and Hämäläinen, 2008) and in a reservoir in Minnesota, U.S.A
48
(McEwen and Butler, 2010). The former study compared 11 lakes with different regulation
49
amplitudes to 12 unregulated lakes by sampling macroinvertebrates in the upper and lower
50
littoral zones, and the authors suggested that freezing and flushing of sediments (and possibly
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nutrients) in late winter led to an impoverished upper littoral macroinvertebrate fauna in the
52
regulated lakes. The latter study assessed changes in littoral macroinvertebrate assemblages
53
using a before-after control-impact design with an unregulated lake as a control system
54
before and after implementation of a new water level regime in a reservoir. Reduced
55
macroinvertebrate densities at 1-2 m depths, driven by losses of amphipods and chironomids,
56
were observed in the reservoir relative to the control lake.
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Composition and abundance of benthic macroinvertebrates in lakes and reservoirs
58
typically vary considerably with water depth, with species richness generally being higher
59
near the shore than in deep waters (Strayer, 2009; Ngupula and Kayanda, 2010). Baumgärtner
60
et al. (2008) observed a gradual transition in macroinvertebrate community structure with
61
water depth, with pronounced similarities within a depth zone and marked differences
62
between adjacent zones. These authors suggested that availability of food resources may
63
explain the observed patterns because water depth affects temperature, oxygen concentration
64
and light availability which, in turn, determine photosynthetic rates and thus food availability.
65
In some deep Mediterranean lakes, intense water use for irrigation coupled with
66
summer drought has resulted in reduced lake volumes and increased eutrophication (Wantzen
67
et al., 2008). Indeed, hypolimnion thickness can be markedly related to lake water level, with
68
hypolimnic concentrations of nutrients (total phosphorus and ammonium) and hydrogen
69
sulphide increasing as water level declines (Zohary and Ostrovsky, 2011). These reductions
70
in water quality may lead to changes in macroinvertebrate communities that can influence
71
energy flow and nutrient cycling (Hansen et al., 1998; Rachamim et al., 2010), with pervasive
72
effects on lake ecosystem health.
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In the Philippines, there has been no research on the impact of fluctuating water levels
74
on the littoral benthic macroinvertebrate communities of lakes and reservoirs, in spite of the
75
fact that such water-level manipulations are common. In the main island of Luzon there are
Page 4 of 29
seven major reservoirs, including the Pantabangan Reservoir in the province of Nueva Ecija.
77
The Pantabangan Reservoir is the country’s largest reservoir. It can store up to three billion
78
cubic meters of water, irrigates more than 100,000 hectares of rice lands in Central Luzon
79
and generates 132 megawatts of hydroelectric power (Philippine National Power
80
Corporation, 2010). Marked water level fluctuations occur because of these uses, especially
81
during intense water use for irrigation during the dry season. The annual water-level
82
fluctuation amplitude (maximum minus minimum water levels) ranges from 11.1 m to 41.5
83
m, with an average of 23.8 (±1.2) m over the last 30 years. A recent study of precipitation
84
patterns in the Pantabanagan-Carranglan catchment (Lasco et al., 2010) revealed an
85
increasing frequency in the occurrence of droughts associated with El Niño episodes over the
86
last 20 years. The same authors also reported that the timing of the onset of the rainy season
87
has become more variable since 2000. These changes in climatic patterns are expected to
88
increase the magnitude of the water level fluctuations occurring in the Pantabangan
89
Reservoir.
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Against this background, our main objective was to examine how water level fluctuations in the Pantabangan Reservoir affect physicochemical water quality and the
92
benthic macroinvertebrate community. At a more general level, this work also represents one
93
of the first scientific investigations of the benthic macroinvertebrate community in the littoral
94
zone of a reservoir in the Philippines. Based on the related research cited above, we predicted
95
that (i) physicochemical water quality and benthic macroinvertebrate community health will
96
be lower when water levels in the reservoir are low, (ii) water quality will decline with
97
increasing water depth, regardless of the overall water level in the reservoir, and (iii) water
98
quality and benthic macroinvertebrate community health will decrease more strongly with
99
water depth when water levels in the reservoir are low.
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Methods
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Study site
103
The Pantabangan Reservoir (15o 48’ 52” N, 121o 06’ 29” E) is situated at 230 m a.s.l., has a
104
surface area of 8,900 ha and a maximum depth of 28.9 m (Guerrero, 1988), and its surface
105
waters are eutrophic (mean total P concentration measured 19 times between 2012 and 2014
106
= 102.3 ± 5.6 µg/L; F.S. Magbanua, unpublished data). The catchment supplying water to the
107
reservoir covers 853 km2 and is located in the townships of Pantabangan and Carranglan
108
(Nueva Ecija province), Alfonso Castañeda and Dupax del Sur (Nueva Vizcaya), and Maria
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Aurora (Aurora) (Lasco et al., 2010). Agriculture and fisheries are the main sources of
110
livelihood in the catchment. Slash-and-burn farming and charcoal making are still practiced
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by the majority of the residents in the catchment.
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The catchment has a climate with two pronounced seasons: dry from November to April and wet during the rest of the year. The average monthly rainfall from 1980-2010 in the
114
town of Pantabangan was 162.2 (± 42.1, standard error) mm, with the highest monthly
115
averages (> 300 mm) from July to September and the least rainfall (< 16 mm per month)
116
from January to March (Fig. 1A). The average water level in the reservoir during the same
117
period was 193.3 (± 2.2) m, and variations in water levels were seasonal and related to
118
monthly precipitation. The reservoir’s annual water level cycle is characterized by four
119
different phases, each with a duration of three months. The maximal water level phase
120
(hereafter called ‘high water level’) lasts from October to December, the emptying phase
121
from January to March, the minimal level phase (hereafter called ‘low water level’) from
122
April to June, and the refilling phase from July to September (Fig. 1A).
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Fig. 1. Mean rainfall in the catchment and water level in Pantabangan Reservoir for the
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period 1980-2010 (A) and for the study year, 2012 (B). Data are from the Philippine National
129
Irrigation Administration – Upper Pampanga River Integrated Irrigation Systems (NIA–
130
UPRIIS).
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131
133
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In our study, we sampled the reservoir on three dates each during the low water level phase (18-19 May, 13-14 June and 6-7 July 2012) and the high water level phase (26-27
135
October, 23-24 November and 8-9 December 2012) (Fig. 1B). Five sites were selected in the
136
reservoir: one in the lacustrine zone (P1; stagnant water; near the dam), one in the
137
intermediate zone (P2), and three in the riverine zones (slowly flowing water) of the main
138
tributaries located in the northern (Carranglan River, P3; Diamman River, P4) and southern
139
(Digoliat and San Juan rivers, P5) basins of the reservoir (Fig. 2). At each site, a single
140
transect was drawn perpendicular to the coastline. Along this transect, four water depths were
141
sampled (1, 5, 10 and 15 m) using a boat. The sampling depth was determined by lowering a
142
weighted line from the boat until it touched the bottom of the reservoir. Because the average
143
water level of the reservoir in 2012 increased from 206.6 (± 2.8) m in May-July to 212.7 (±
144
0.1) m in October-December, water depth (surface to bottom) at a given set of coordinates in
145
a given transect was not constant during our return visits. Thus, in terms of their positions
146
along the sampling transect at each site, the 1-m and 5-m depth sampling points during the
147
low water level period corresponded approximately to the 10-n and 15-m depth sampling
148
points during the high water level period. For the study year, 2012, the average water-level
149
fluctuation amplitude (maximum minus minimum water levels, determined monthly) was
150
35.1 (± 18.2) m, a value somewhat higher than the mean long-term annual fluctuation
151
amplitude (23.8 m; see above).
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Fig. 2. Map of Pantabangan Reservoir showing the locations of the five sampling sites.
155 156
Page 9 of 29
Water physicochemistry
158
Dissolved oxygen (DO), temperature (°C), pH, conductivity, and total dissolved solids (TDS)
159
were measured onsite at total water depths (surface to bottom) of 1, 5, 10 and 15 m using a
160
multi-parameter water quality meter (YSI Professional Plus; Yellow Spring Instruments,
161
Ohio, USA). At each site, these parameters were measured along the single transect at the
162
above four water depths, and the four sampling points were always 20-100 m apart. At the 5-
163
m depth sampling point, water samples were collected both at 1 m and at 5 m below the
164
surface. Similarly, at the 10-m and 15-m depth sampling points, water samples were collected
165
at 1, 5 and 10 m below the surface and at 1, 5, 10 and 15 m below the surface, respectively.
166
Water transparency (in m) was also determined with a 20 cm-diameter, black-and-white
167
Secchi disk submerged below the surface. Moreover, three 500-mL water samples for total
168
nitrogen (TN) and total phosphorus (TP) analyses were collected using a Van Dorn-style
169
sampling bottle at each water depth mentioned above. All measurements were conducted
170
between 9:00 am and 2:00 pm. These water samples were transported to the laboratory in a
171
container covered with ice and stored at -20oC until analysis using standard, colorimetric
172
protocols (APHA, 1999).
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Benthic macroinvertebrates
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At each sampling site and depth, three benthic macroinvertebrate samples (distance between
176
samples about 5 m) were collected using an Ekman dredge (surface area 60 x 35 cm). A total
177
of 360 samples were collected: 3 samples x 5 sites x 4 water depths x 6 sampling dates (3
178
during the low water level period and 3 during the high water level period). All materials
179
including sediments collected in the dredge were emptied to a 12-L sieve bucket (600- µm
180
mesh, WaterMark® sieve bucket, Forestry Suppliers, Inc., Jackson, MA). Filtered materials
181
were preserved in 90% ethanol in the field. In the laboratory, samples were further washed
Page 10 of 29
and elutriated using a 250-µm sieve to separate macroinvertebrates from plants and inorganic
183
materials. Macroinvertebrates were identified and counted under a stereo microscope (Zeiss
184
STEMI 2000c, magnification 6.5-50x; Carl Zeiss, Göttingen, Germany). Identification was
185
done to the lowest feasible taxonomic level using keys of Dudgeon (1999) and Yule and
186
Yong (2004). Invertebrate numbers per sample were extrapolated to densities per square m.
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Prior to data analysis, the values from the samples collected on the three dates within each water level period were averaged, resulting in 60 data points per water level period.
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Data analysis
191
Where necessary, data were log10 (x) or log10 (x + 1) transformed to improve normality and
192
homoscedasticity after exploratory data analysis. All analyses (repeated-measures, nested
193
two-way (M)ANOVAS with block factor) were conducted in IBM SPSS Statistics 20.0 (IBM
194
Corp., New York, USA). In this model, sampling depth (4 levels) was a fixed main (between-
195
subjects effects) factor while water level (low vs high) was a repeated-measures (within-
196
subjects contrasts) factor. In addition, sampling location (the five sites) was a block factor
197
and sample number (1-3) a nested factor (nested within sampling depth).
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For macroinvertebrate community-level metrics (macroinvertebrate density, taxon
199
richness, Simpson’s diversity and evenness) and most water physicochemistry variables
200
(temperature, DO, conductivity, TDS, TN and TP), the ANOVA model was intercept (d.f. 1)
201
+ depth (3) + sample (depth) (8) + block [4] + error [44; n = 60] for tests of between-subjects
202
effects and time (d.f. 1) + time x depth (3) + time x sample (depth) (8) + time x block [4] +
203
error (time) [44] for tests of within-subjects effects. For water transparency, all values
204
determined above the 1-m depth sampling locations were zero (due to uniformly high
205
turbidity at these shallow locations). Therefore, only the data collected above the 5, 10 and
206
15 m sampling locations were analyzed for this variable.
Page 11 of 29
207
If between-subjects effects were significant, pairwise comparisons was performed for the factor depth using post hoc tests (Tukey’s HSD). To determine changes in community
209
composition, the densities of the eight most common invertebrate taxa were analyzed with the
210
multivariate equivalent of the ANOVA model above. All presented results were significant
211
(P < 0.05). Results for the block factor (sampling site) are not shown because any significant
212
effects merely represent background variation that is unimportant for our research objectives.
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Results
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Water quality
216
Except for DO and TN, all water quality variables measured differed markedly between water
217
levels (Table 1). Water transparency, pH, TDS and TP were highest during the high water
218
level period whereas temperature and conductivity were highest during the low water level
219
period. Water temperature, DO and TN also differed across sampling depths (Table 1).
220
Temperatures at 1 m or 5 m water depth (range 26.7–31.5oC) were warmer than at 10 m or 15
221
m (range 24.4–30.3oC). DO was higher at 1 m than at 15 m depth, whereas TN showed the
222
opposite pattern. None of the water quality variables showed an interaction between water
223
level and sampling depth (Table 1).
225 226 227
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228 229 230 231
Page 12 of 29
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Table 1. Summary (means ± SE and P-values) of the ANOVAs comparing water quality variables between water level periods (within-subjects factor) and
233
sampling depths (between-subjects factor). N = 60, except for water transparency where n = 45. DO – dissolved oxygen, TDS – total dissolved solids, TN –
234
total nitrogen, TP – total phosphorus. Rankings for specific contrasts or post hoc tests in cases with significant effects are given. P-values < 0.05 are in bold
235
print. Water level
Response variable
P-
M
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232
Low
High
1.6 (0.1)
2.2 (0.2)
<0.001
Temp. (0C)
29.6 (0.1)
27.2 (0.1)
<0.001
DO (mg L-1)
6.6 (0.9)
5.7 (0.1)
0.927
pH
8.7 (0.1)
9.7 (0.2)
<0.001
132.8 (2.4)
128.7 (1.2)
(µS cm-1)
Ac
Conductivity
Ranking
P-value
5m
10 m
15 m
High > Low
-
2.4 (0.2)
2.6 (0.1)
2.5 (0.1)
0.465
Low > High
28.8 (0.3)
28.7 (0.3)
28.2 (0.3)
27.9 (0.3)
<0.001
(1 = 5) > (10 = 15)
0.731
8.0 (1.8)
5.9 (0.2)
5.8 (0.3)
5.0 (0.3)
0.002
1 > 15
0.539
High > Low
9.0 (0.2)
9.0 (0.2)
8.7 (0.3)
8.8 (0.3)
0.395
0.400
<0.001
Low > High
127.4 (4.5)
133.4 (2.4)
130.7 (0.9)
131.5 (1.3)
0.575
0.402
High > Low
0.07 (0.004)
0.07 (0.004)
0.09 (0.02)
0.07 (0.003)
0.385
0.452
8.9 (1.3)
10.2 (1.2)
11.9 (1.3)
12.8 (1.2)
0.002
0.18 (0.05)
0.34 (0.11)
0.40 (0.13)
0.30 (0.09)
0.341
ed
pt
parency (m)
ce
Water trans-
P-
1m
value
Contrast
Interaction
Sampling depth
TDS (mg L-1)
0.05 (0.003)
0.09 (0.01)
<0.001
TN (mg L-1)
10.7 (0.7)
11.1 (1.0)
0.568
TP (mg L-1)
0.01 (0.001)
0.60 (0.09)
<0.001
High > Low
value
236 237
Page 13 of 29
0.623
(10 = 15) > 1
0.102 0.367
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Table 2. Summary (means ± SE and P-values) of the (M)ANOVAs comparing invertebrate responses (n=60) between water level periods (within-subjects
239
factor) and sampling depths (between-subjects factor). Rankings for specific contrasts or post hoc tests in cases with significant effects are given. P-values <
240
0.05 are in bold print. Response variable
Water level PHigh value
89 (24)
576 (69)
<0.001
Tanypodinae
36 (7)
163 (34)
<0.001
Bezzia spp.
1 (1)
88 (15)
<0.001
Caenodes spp.
1 (1)
34 (9)
<0.001
pt
ce
23 (5)
<0.001
0 (0)
16 (4)
<0.001
Ac
Glossosoma spp.
0 (0)
Corbicula fluminea
19 (7)
0 (0)
<0.001
Tarebia granifera
9 (4)
1 (1)
0.041
Community-level metrics Invertebrate density 166 (29.2) Taxon richness Simpson’s diversity
Interaction
1m
5m
10 m
15 m
P-value
Ranking
P-value
High > Low
451 (119)
455 (79)
295 (72)
129 (39)
0.043
5 > 15
0.141
High > Low
46 (16)
207 (63)
97 (25)
48 (12)
<0.001
5 > 15 > 1; 10 > 1
0.331
High > Low
10 (4)
66 (20)
73 (23)
29 (8)
<0.001
(5 = 10 = 15) > 1
0.001
High > Low
16 (9)
29 (10)
4 (2)
20 (13)
0.013
5 > (10 = 15)
0.049
High > Low
11 (6)
22 (7)
11 (4)
1 (1)
0.010
5 > 15
0.010
High > Low
1 (1)
6 (3)
11 (5)
13 (5)
0.146
Low > High
3 (2)
3 (3)
27 (14)
6 (3)
0.036
10 > 5
0.036
Low > High
10 (7)
10 (4)
0 (0)
0 (0)
0.023
5 > (10 = 15)
0.221
ed
Chironominae
Sampling depth
Contrast
M
Low
Community composition
Ecnomus spp.
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0.146
918 (96.8)
<0.001
High > Low
557 (144)
820 (147)
533 (99)
257 (62)
0.014
5 > 15
0.802
1 (0.2)
4 (0.2)
<0.001
High > Low
2 (0.3)
4 (0.4)
3 (0.3)
2 (0.3)
<0.001
5 > 15 > 1; 10 > 1
0.937
1.2 (0.2)
2.7 (0.2)
<0.001
High > Low
1.1 (0.2)
2.0 (0.2)
2.4 (0.3)
2.4 (0.4)
0.002
(5 = 10 = 15) > 1
0.651
Page 14 of 29
i cr 0.8 (0.1)
0.001
High > Low
us
0.5 (0.1)
0.6 (0.1)
0.6 (0.1)
0.7 (0.1)
0.8 (0.1)
0.304
an
Simpson’s evenness
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ed
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241
Page 15 of 29
0.162
Benthic macroinvertebrates
243
A total of 1508 individuals belonging to 20 taxa were recorded in the 360 samples. Of these,
244
eight comprised 91.2% of the total: the two midge subfamilies Chironominae (28.6%) and
245
Tanypodinae (23.3%), the ceratopogonid Bezzia spp. (14.2%), the mayfly Caenodes spp.
246
(7.2%), the caddis flies Ecnomus spp. (6.6%) and Glossosoma spp. (5.0%), the Asian clam
247
Corbicula fluminea (4.1%) and the Asian prosobranch snail Tarebia granifera (2.2%). The
248
MANOVA based on these eight common taxa showed significant overall effects of water
249
level (Wilk’s λ P < 0.001), sampling depth (Wilk’s λ P < 0.001) and an overall interaction of
250
the two factors (Wilk’s λ P = 0.002). Furthermore, the between-subjects effects in the
251
MANOVA revealed that the overall effect of water level was due to effects of water level on
252
all eight individual taxa (Table 2). Of these, only C. fluminea and T. granifera had a higher
253
density during the low water level period, while the other six taxa showed higher densities
254
during the high water level period. The significant overall effect of sampling depth was due
255
to the density patterns of seven taxa (Chironomidae, Tanypodinae, Bezzia, Caenodes,
256
Ecnomus, C. fluminea and T. granifera); except for C. fluminea, all these displayed their
257
highest densities at 5 m depth (Table 2). In addition, Bezzia, Caenodes, Ecnomus and C.
258
fluminea displayed interactions of water level and sampling depth. During the low water level
259
period, densities of the first three taxa were uniformly low or absent across the four sampling
260
depths. During the high water level period, by contrast, densities of the same three taxa were
261
much higher and also varied considerably across sampling depths (Fig. 3), with density
262
maxima always occurring either at 5 m or 10 m. For C. fluminea, the shape of the interaction
263
was different: this taxon was completely absent at high water level, whereas its densities
264
varied across sampling depth at low water level, peaking at 10 m depth (Fig. 3).
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16 Page 16 of 29
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Fig. 3. Density patterns for common benthic invertebrate taxa due to the interaction of water
270
level (low: ●; high: ■) and sampling depth in Pantabangan Reservoir.
271
Page 17 of 29
While none of the community-level macroinvertebrate metrics showed a significant
273
interaction effect, values for all four metrics were higher during the high water level period
274
and all metrics except for evenness differed across sampling depths (Table 2). Both total
275
macroinvertebrate density and taxon richness were highest at 5 m depth, whereas Simpson’s
276
diversity was highest at 10 and 15 m.
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Discussion
278
High versus low water level periods
280
Our prediction that both physicochemical water quality and benthic macroinvertebrate
281
community health should generally be lower when water levels in the Pantabangan Reservoir
282
are low was largely supported by our findings, especially for the macroinvertebrates. Ten of
283
the 12 studied individual macroinvertebrate response variables showed significantly lower
284
values. Water temperature and conductivity were also higher during the low water level
285
period, presumably due to increased air temperatures and reduced water inflow volumes.
286
Several earlier studies in reservoirs in both tropical and subtropical climates have shown
287
inverse relationships between lake/reservoir water level and water temperature (Dejenie et al.,
288
2008; Zhu et al., 2013), and also between water level and conductivity (Dejenie et al., 2008;
289
Jose de Paggi and Devercelli, 2011). In their study of the environmental factors influencing
290
macroinvertebrate communities in the littoral zone of a reservoir in Zimbabwe, Dalu et al.
291
(2012) attributed the increase in conductivity during low water level periods to evaporation-
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driven concentration of dissolved substances in the water.
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A similar pattern of decreases in macroinvertebrate taxon richness, density and
294
abundance as in our study has been observed in another tropical reservoir in China due to
295
increased water level fluctuations (Zhang et al., 2012). As pointed out in the Introduction,
296
limited studies have been done in tropical and subtropical lakes and reservoirs on the effects
Page 18 of 29
of changing water levels on macroinvertebrate communities compared to their temperate
298
counterparts. Most of the research in the tropics and subtropics that directly examined the
299
effects of water drawdown focussed on macrophyte (e.g. Boschilia et al., 2012) or plankton
300
(e.g. Okogwu and Ugwumba, 2012) assemblages.
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Water quality and water depth
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Our hypothesis that water quality should decline with increasing sampling depth, regardless
304
of the overall water level in the reservoir, was partly supported. Only three of the eight
305
studied physicochemical metrics showed the predicted patterns: water temperature and
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dissolved oxygen readings were highest at the two shallowest water depths while total
307
nitrogen concentration was highest at the greatest depth sampled. While we did not measure
308
actual lake stratification in Pantabangan Reservoir, we observed an increasing pattern of total
309
nitrogen concentrations in parallel with water depth. Nonetheless, nutrient inputs from the
310
surrounding catchment (e.g. dissolved nutrients entering the reservoir via tributary inflows
311
and surface runoff from fertilized pasture and croplands) may have contributed in the
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increased nutrient concentrations in the reservoir.
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Thermal stratification is typical for many standing waters, including Philippine lakes,
314
due to the warming of surface water by the sun while the deeper water layers remain colder
315
by comparison (Santiago and Arcilla, 1993). Moreover, the dissolved oxygen pattern we
316
observed was consistent with the clinograde curve typical for eutrophic lakes (Wetzel, 2001)
317
and reservoirs (e.g. Araújo et al., 2011). This pattern has also been observed in Lake
318
Sampaloc (Santiago and Arcilla, 1993), a natural lake located in the same region of the
319
Philippines as Pantabangan Reservoir.
320 321
Vertical mixing and turnover are important processes in the nutrient cycling in many deep lakes and reservoirs (Boehrer and Schultze, 2008). However, this vertical nutrient
Page 19 of 29
exchange is relatively weak in tropical and subtropical regions due to warm climates, in
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contrast to temperate climates where inverse stratification is common (Peng et al., 2012).
324
Similarly, Lewis (2000) reported that deep mixing happens only occasionally in tropical lakes
325
and reservoirs because wind-driven surface waves are rarely strong enough to mix epilimnion
326
and hypolimnion completely. Nonetheless, river inflows and precipitation in the catchment
327
play a critical role in causing changes in mean water depth and nutrient inputs in tropical and
328
subtropical reservoirs (Lewis, 2000; Peng et al., 2012).
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Interactive effects of water depth and water level period
331
Our final hypothesis predicted that water quality and benthic macroinvertebrate community
332
health should decrease more strongly with water depth when water levels in the reservoir are
333
generally low. This hypothesis received no support for water physicochemistry but was
334
unequivocally supported for macroinvertebrate community health. These contrasting findings
335
suggest that other, unmeasured factors may be influencing the macroinvertebrate community.
336
Of the eight investigated water physicochemistry variables, only temperature showed
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a significant interaction between water level and sampling depth, and the observed interaction
338
pattern for temperature cannot be interpreted readily as a reduction in water quality as other
339
factors may be involved. Lewis (2000) noted that at high temperatures, the ability of water in
340
tropical lakes to hold oxygen decreases and that oxygen is further removed more rapidly at
341
the hypolimnion because of high rates of microbial metabolism at high temperatures.
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Of the macroinvertebrate variables, none of the four individual community-level
343
metrics displayed overall significant interactions, but macroinvertebrate community
344
composition determined with the MANOVA did. This overall interaction was due to the
345
distributions of four of the eight most common macroinvertebrate taxa (Bezzia, Caenodes,
346
Ecnomus and C. fluminea), the first three of which all showed a similar pattern. While this
Page 20 of 29
pattern (low water level: uniformly low abundance or absent across all four depths; high
348
water level: more abundant, with density maximum at 5m or 10m depth) did not quite follow
349
the shape we had predicted, it can still be interpreted as indicating that macroinvertebrate
350
community health declined more markedly (in relative terms) with sampling depth when
351
water level in the reservoir was low than when water level was high. These findings contrast
352
with those from two reservoirs in subtropical China (Hu et al., 2012; Liu et al., 2012;
353
although both studies did not directly examine the effects of water level fluctuations) where
354
macroinvertebrate density generally declined with water depth. The former authors identified
355
water depth as the only physicochemical variable with the potential to explain this density
356
pattern, while the latter authors suggested this decline could be due to thermal stratification.
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Seasonal variations in physicochemistry and the potential role of total phosphorus
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Generally, most environmental parameters exhibit seasonal variations (Araújo et al., 2011;
360
Dalu et al., 2012; Zhang et al., 2014), and these variations may influence the distribution and
361
abundance of macroinvertebrate communities. Our study found differences in several water
362
physicochemistry parameters in parallel with water level fluctuations. We acknowledge that
363
these differences may also be related to seasonal variation (as discussed earlier on). However,
364
total nitrogen and dissolved oxygen concentrations showed no significant seasonal changes in
365
our study, consistent with the findings of Zhang et al. (2014) for the Xinlicheng Reservoir in
366
China (for total nitrogen) and of Oliveira et al. (2014) for the Apipucos Reservoir in
367
Pernambuco, Brazil (for both total nitrogen and dissolved oxygen). Moreover, in a recent
368
study of three tropical reservoirs in the Paraopeba River catchment in southern Brazil,
369
patterns in the macroinvertebrate community were unrelated to seasonal changes in
370
temperature and rainfall (Molozzi et al., 2013). Further, total phosphorus concentration in the
371
Funil Reservoir in Southern Brazil was higher during the wet season, presumably due to
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Page 21 of 29
increased inflow of nutrient-rich water from the river feeding the reservoir (Araújo et al.,
373
2011). By contrast, in our study total phosphorus concentration was higher during the dry
374
season, possibly because a larger area of reservoir sediment was inundated (perhaps leading
375
to nutrient resuspension) as this season coincided with relatively higher water level in the
376
reservoir. The relatively low concentration during the wet season may have been due to
377
higher water inflows resulting in increased flushing thus improving the reservoir’s water
378
quality. Consequently, there is some circumstantial evidence that total phosphorus
379
concentration may have played a role in creating the macroinvertebrate responses we
380
observed. Nonetheless, we cannot reliably identify the observed difference in total
381
phosphorus as a cause for the variations that we detected in the macroinvertebrate
382
community. This is due to the observational nature of our study and because most of the
383
widely used macroinvertebrate taxonomic metrics, community descriptors and indices in
384
standing waters including river-reservoir systems are generally poorly correlated with total
385
phosphorus (e.g. McGoff et al., 2013), pH, total dissolved solids and conductivity (e.g.
386
Yazdian et al., 2014).
388
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Conclusions
389
Our study, albeit observational in nature, is one of relatively few existing assessments of the
390
potential effects of water-level fluctuations on benthic macroinvertebrate communities in
391
standing freshwaters in the tropics. We recognize that our study is unreplicated at the
392
reservoir scale and also lacks a suitable control (an unregulated lake) for comparison.
393
Nevertheless the study represents a significant gain in knowledge, especially for the
394
Philippines, because there has been no previous research on this topic in Philippine lakes and
395
reservoirs.
Page 22 of 29
396
Regarding the Pantabangan Reservoir, hopefully the data presented in our paper will be complemented in the future by regular monitoring of water quality and benthic
398
macroinvertebrate community health. Investigations in the major tributaries draining into the
399
reservoir are now underway, to determine the influence of river flows, dissolved and
400
suspended solids from soil erosion and possible water quality degradation because of
401
agricultural activities in the catchment. However, it is also important to investigate the
402
general limnology of the reservoir because currently much of the basic information is lacking,
403
including stratification regime, bathymetry, and annual dynamics of the key chemical
404
parameters at different depths along the water column. In the longer term, such information
405
will be able to guide resource managers and local authorities in the development of a science-
406
based catchment-scale restoration program. This program should include a management
407
strategy to limit or reduce inputs of nutrients, fine sediment and other pollutants in order to
408
protect and enhance freshwater biodiversity in the reservoir.
410
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Acknowledgments
We thank the First Gen Hydro Power Corporation, Diliman Science Research Foundation and
412
the Institute of Biology, University of the Philippines Diliman for financial and logistical
413
support. We also acknowledge the National Irrigation Administration – Upper Pampanga
414
River Integrated Irrigation Systems (NIA–UPRIIS) for providing the 1980 to 2012 water
415
level and rainfall data for the Pantabangan Reservoir. Special thanks to our colleagues from
416
the First Gen Hydro Power Corporation and the Institute of Biology for their help in the field,
417
Jovilyn Fernandez and Paolo Garrido for their help with Figure 2, and Daniel Hering and an
418
anonymous reviewer for helpful comments on the manuscript.
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