Nutrient loadings and deforestation decrease benthic macroinvertebrate diversity in an urbanised tropical stream system

Limnologica 80 (2020) 125744

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Nutrient loadings and deforestation decrease benthic macroinvertebrate diversity in an urbanised tropical stream system

T

Elfritzson M. Peraltaa,b,c,*, Leocris S. Batucan Jrd,i, Irisse Bianca B. De Jesusa,c, Ellis Mika C. Triñoc, Yoshitoshi Ueharaf, Takuya Ishidaf, Yuki Kobayashif, Chia-Ying Kog, Tomoya Iwatah, Adelina S. Borjae, Jonathan Carlo A. Brionesa,b,c, Rey Donne S. Papaa,b,c, Francis S. Magbanuad, Noboru Okudaf a

The Graduate School, University of Santo Tomas, España Boulevard, Manila, 1015, Philippines Research Center for the Natural and Applied Sciences, University of Santo Tomas, España Boulevard, Manila, 1015, Philippines Department of Biological Sciences, College of Science, University of Santo Tomas, España Boulevard, Manila, 1015, Philippines d Institute of Biology, University of the Philippines Diliman, Quezon City, 1101, Philippines e Resource Management and Development Department, Laguna Lake Development Authority, East Avenue, Diliman, Quezon City, 1101 Philippines f Research Institute for Humanity and Nature, 457-4 Motoyama, Kamigamo, Kyoto, 603-8047, Japan g Institute of Fisheries Science and Department of Life Science, National Taiwan University, Taipei, 10617, Taiwan h Department of Environmental Sciences, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan i Biodiversity Program, Taiwan International Graduate Program, Academia Sinica and Department of Life Science, National Taiwan Normal University, Tingzhou Road, Taipei, 11529, Taiwan b c

ARTICLE INFO

ABSTRACT

Keywords: Philippines Human population density Canopy openness Cultural eutrophication EPT Southern urban hydrosystem syndrome

Lotic ecosystems in urban areas are severely impacted by anthropogenic environmental stressors, such as deforestation and nutrient pollution, due to socioeconomic activities in the catchment. To work out measures for identification and mitigation of concurrent multiple stressors to a stream system, it is necessary to assess the relative importance of impacts by the individual stressors. Here we aimed to discriminate the covarying effects of nutrient pollution and deforestation on benthic macroinvertebrate communities in an urbanised tropical stream system. In the Silang-Santa Rosa Subwatershed (SSRS) of Laguna de Bay, benthic macroinvertebrates and physicochemical environments were investigated at 13 sites varying in human population density, riparian canopy, and land-use pattern as indicated by geographic information systems in the catchment. Regression and multivariate analyses were performed to identify the drivers of the biodiversity loss and understand its underlying mechanisms. In the SSRS, where rapid economic growth took place without updating poorly installed wastewater treatment plants (WWTPs), domestic activity indicated by human population density in the catchment was the primary factor in generating heavy phosphorous loadings (mean total phosphorus = 0.91; range = 0–1.50 mg/l) that caused hypoxia (mean dissolved oxygen = 2.98; range = 0.13–6.27 mg/l) in stream waters and subsequently reduced macroinvertebrate diversity (mean H’ = 0.91; SD ± 0.61). Nutrient and organic pollution and riparian deforestation explained 53.5% and 9.7% of the variation of benthic macroinvertebrate communities in SSRS, respectively. Given such scenario, additional WWTPs servicing urban developments and improved riparian canopy cover in the SSRS can be both strategic and cost-effective in the initial steps of environmental mitigation in urbanised streams, especially in rapidly developing countries.

1. Introduction Running water ecosystems are expressions of their terrestrial environments (Hynes, 1970; Sioli, 1975). Consequently, all impacts in the catchment imprint on its environmental health (Greig et al., 2012; Freitag et al., 2016). Globally, 65% of lotic ecosystems are estimated to

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be under environmental threats such as habitat destruction and biodiversity loss (Vörösmarty et al., 2010). Landscape changes, to accommodate population growth and economic developments, strongly influence stream ecosystems and biodiversity (Allan, 2004). Streams in fast-developing countries often encounter the unfavourable effects of deforestation and siltation (Iwata et al., 2003; Wantzen and Mol, 2013)

Corresponding author at: Department of Biological Sciences, College of Science, University of Santo Tomas, España Boulevard, Manila, 1015, Philippines. E-mail address: [email protected] (E.M. Peralta).

https://doi.org/10.1016/j.limno.2019.125744 Received 27 February 2019; Received in revised form 26 October 2019; Accepted 28 October 2019 Available online 13 December 2019 0075-9511/ © 2019 Elsevier GmbH. All rights reserved.

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Fig. 1. Silang-Santa Rosa Subwatershed sampling sites showing surrounding land cover and the prevailing land use.

in their rural upper zones and urbanisation in their lower sections (Malmqvist and Rundle, 2002; Pauchard et al., 2006; Ramírez et al., 2012). However, the relative impacts of riparian deforestation and catchment urbanisation within a watershed have rarely been discriminated because they usually covary (Walsh et al., 2007). Deforestation rates in the tropics have alarmingly increased by 53% between 2001 and 2012, from 6000 kha in 2001 to 9200 kha in 2012 (Hansen et al., 2013; Austin et al., 2017). Furthermore, myriad urban streams have been exposed to physical, chemical and biological environmental stressors collectively termed ‘urban stream syndrome’, which includes geomorphic and hydromorphic stream alterations resulting in flashier hydrograph, increased channel width, reduced base flow and elevated nutrient and contaminant levels (Walsh et al., 2005; Roy et al., 2009a). Although this condition is prevalent worldwide, its mechanisms and ecological consequences vary among locations and climate regimes (Ramírez et al., 2009; Roy et al., 2009a, b; Hale et al., 2016; Utz et al., 2016). For example, riparian canopy structure varies from temperate to tropical regions in terms of primary productivity, inputs of organic matter and sedimentation in streams (Bernot et al., 2010; Dodds et al., 2015). Also, human developments, as indicated by human population densities, which may indirectly affect stream response to urbanisation, are generally highest in low altitudes and the tropics (Cohen and Small, 1998; Olson et al., 2015). With landscape conversion ongoing in the tropics, these two impact types (i.e. deforestation and urbanisation) converge in the cities, adversely impacting stream ecosystems and macroinvertebrate diversity (Walsh et al., 2001; Walsh, 2006; Liu et al., 2016; Gimenez and Higuti, 2017; Martins et al., 2017; Weliange et al., 2017) through habitat degradation (Iwata et al., 2003; Getwongsa et al., 2010; Hepp et al., 2010; Egler et al., 2012) and changes in available food sources (Benstead et al., 2003; Benstead and Pringle, 2004). While a vast amount of knowledge on these phenomena has been collected in temperate and long-developed countries, these

phenomena are less known in the tropics and developing countries, despite being the current site of the fastest rates of deforestation and urban sprawl (Capps et al., 2016). In human-impacted streams, a recurrent issue that strongly reduces water quality is the intense discharge of domestic sewage by human settlements and urban-related activities. Untreated residential wastewaters, industrial discharges and agricultural activities contribute to nitrogen and phosphorus enrichment in lotic systems (Smith, 2003; Dodds, 2006; Karaer and Küçükballi, 2006). Consequently, such nutrient enrichment causes rapid cultural eutrophication and may, in turn, lead to compositional changes in benthic macroinvertebrate assemblages (Moreno and Callisto, 2006; Sánchez-Argüello et al., 2010; De Jesús-Crespo and Ramírez, 2011). For instance, phosphorus which is already naturally present in limiting amounts than the other essential elements in streams (Correll, 1998) has been shown to limit primary production in tropical streams (Pringle et al., 1986; Larned and Santos, 2000). In effect, gradients of phosphorus were demonstrated to primarily shape sensitive and tolerant benthic macroinvertebrate taxa assemblages in stream systems (Ramírez and Pringle, 2006; Friberg et al., 2010). The urban stream syndrome shows disparities between different climatic regions (Booth et al., 2016). In a recent review, Wantzen et al. (2019) have identified specific elements of urban lotic and lentic ecosystems in developing countries (including the Philippines) and defined the ‘Southern urban hydrosystem syndrome’, which does not only include the highly variable geophysical setting, but also the eco-social aspects of urban hydrosystem restoration and management in the Global South. With our study, we embrace the opportunity to contribute in the global perspectives on urban hydrosystem syndrome by further exploring how multiple stressors can alter stream systems in a tropical setting. Given the described situations in the tropics, we expect that increase of riparian deforestation and phosphorus loadings and 2

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their concomitant reduction of allochthonous food sources and oxygen concentration, respectively, could be the most important stressors for benthic communities. As in other streams, Ephemeroptera, Plecoptera and Trichoptera taxa which are composed of diverse feeding guilds are deemed sensitive to environmental disturbances brought by urbanisation. In this study, we aimed to disentangle the overlapping effects of deforestation and nutrient pollution on benthic macroinvertebrate communities of a stream in a rural–urban gradient within the subwatershed of Laguna de Bay near Metro Manila, the Philippines.

obtained using regional Philippine population counts from the 2015 Region 4A Population Census (Philippine Statistics Authority [PSA], 2015) and GIS tools (Quantum GIS version 2.10.1). 2.4. Benthic macroinvertebrate collection and identification Simultaneous with the collection of environmental variables, benthic macroinvertebrates were sampled using a Surber sampler (30 cm x 30 cm; 500 μm mesh). Quantitative samples (two replicates per site) were taken from two randomly selected riffle microhabitats at each monitoring site. Each replicate was pooled together in a labelled plastic bag and stored in a cooler at 5 °C. Within 48 h, samples were sorted and stored in scintillation vials with 95% ethanol. Specimens were viewed under stereo (SMZ 171, Motic Asia, Hong Kong) and compound (BA310, Motic Asia, Hong Kong) microscopes and identified to the lowest possible taxonomic level using appropriate taxonomic keys (Pescador et al., 1995; Epler, 1996; Dudgeon, 1999; Yule and Yong, 2004; Merritt et al., 2008; Sartori et al., 2008; Madden, 2009; Bae, 2010).

2. Methods 2.1. Study area The Silang-Santa Rosa Subwatershed (SSRS) is situated at the western portion of the largest lake in the Philippines, Laguna de Bay. Its centroid geographic coordinates are 14° 13' 44” N latitude, 121° 01' 05” E longitude (Fig. 1). It has a total area of 112.6 km2 with 65.1% and 35.9% being covered by vegetated (e.g. crop, grass, shrub lands) and built-up areas, respectively (Bragais et al., 2014). Deforestation to accommodate human settlements in the upland area of the SSRS took place from the 1920s to the 1940s, resulting in the conversion of primarily forested areas into mainly rice farmlands. The only remaining woody vegetation areas found in the riparian zone were further transformed into residential and industrial areas in the early 1990s. This land conversion led to soil erosion and surface runoff, increasing flood risks in downstream and coastal areas (Engay-Gutierrez, 2015). For monitoring, 13 sites were selected to cover 11 streams of three stream orders with different land use and cover patterns. Geographic coordinates for each site were obtained using a global positioning system unit (Garmin GPSMAP 64 s GPS, Garmin International Inc., Kansas, USA). Our sampling strategy was intended to select sites representative of the entire catchment and its variably dense human population (total population count/km2).

2.5. Data analyses We used a linear or multiple regression analysis (IBM SPSS Statistics 20.0, IBM Corp., New York, USA) to determine which GIS variables best accounted for variation in nutrient concentrations of stream waters. This method also allowed us to examine how such nutrient loads can alter chemical environments as habitat quality for benthic macroinvertebrates, which may, in turn, affect their biodiversity (i.e. diversity index, taxon richness, taxon density). In addition, a non-linear regression analysis was fit to a decay curve to examine how deforestation of riparian forests can depauperate the benthic macroinvertebrate communities (i.e. EPT taxa), incorporating into the regression model the canopy openness as an index of physical disturbance of riparian habitat. To assess the response of macroinvertebrate communities to nutrient pollution and riparian deforestation, we used the Shannon–Wiener diversity index (H'), species richness (S), species density (D), EPT indices, and Chironomidae and Oligochaeta densities. The EPT indices simply computes for species richness or density of the disturbance-sensitive insect orders Ephemeroptera, Plecoptera and Trichoptera, which manifest diverse feeding functions in less-impacted stream ecosystems. Pollution-tolerant communities can be investigated using densities of chironomid and oligochaete taxa. Although structural equation modelling is generally used as a powerful tool to disentangle complex causal relationships between drivers and community responses (McCune and Grace, 2002; Pugesek et al., 2003), it was not applicable to our data set because of small sample size. For similar reasons, this may limit the statistical power of our regression models for detecting indirect effects of human disturbance on stream biodiversity. An initial unconstrained detrended correspondence analysis (DCA) using the abundance data of benthic macroinvertebrates was performed to show whether a unimodal or linear ordination method was appropriate for the demonstration of specific responses of macroinvertebrate taxa to environmental factors. This analysis revealed that the longest gradient length (DCA axis 1: 5.5714) was greater than 3.0, suggesting that unimodal methods were appropriate for exploring compositional variation among benthic macroinvertebrate assemblages (ter Braak and Verdonschot, 1995; Blakely et al., 2014). As such, partial canonical correspondence analysis (pCCA) was used to visualize and describe the relationship between benthic macroinvertebrate species and environmental variables while controlling the possible effect of longitudinal variation (geographical position) on the data set. Prior to pCCA, variables showing high multicollinearity through inspection of variance inflation factors (VIF < 20; ter Braak and Verdonschot, 1995; Palmer, 1993) were omitted (temperature, pH, TDS, salinity) to remove redundant explanatory variables. Rare species were not included in the pCCA. A Monte Carlo permutation test with 499 permutations was

2.2. Environmental variables Environmental variables were obtained at three points in the main channel of each site. Water temperature, total dissolved solids (TDS), salinity, conductivity and pH were measured with a handheld multiparameter metre (EC500; Extech Instruments, NH, USA) and dissolved oxygen (DO) with a DO metre (D0600; Extech Instruments, NH, USA). For nutrient analysis, 1 L of pre-sieved (1 mm mesh) water sample was collected using one plastic bottle per site, stored in a cooler at 5 °C and transported to the Chemistry Department Laboratory of the Laguna Lake Development Authority. Within 24 h, collected water samples were analysed to determine the concentrations of total phosphorus (TP), nitrates (NO3-) and ammonia (NH3) by following standard methods (American Public Health Association [APHA], 2005). To score the actual riparian forest coverage, digital photos (NIKON D7000, Japan) were taken with a fish-eye lens (Sigma 4.5 mm F2.8 EX DC Circular Fisheye HSN, Japan) set up 50 cm above the water surface at three equidistant (25−30 m apart) points for each sampling site. Mean canopy openness as a measure of immediate vegetation in the stream was computed using CanopOn 2.0 software (http://takenaka-akio.org/ etc/canopon2). These environmental variables were all collected from November 2 to 19, 2015, which is an ideal period after the rainy season but before the onset of the dry season. 2.3. GIS data As geographic information system (GIS) variables, we used the proportional areas of built-up, agricultural, shrubland and grassland areas and human population density (HPD) in the catchment of each monitoring site. The HPD data were log-transformed, following Cabana and Rasmussen (1996) and Karube et al. (2010). These variables were 3

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made to assess whether compositional variation among benthic macroinvertebrate assemblages was related to environmental variables on the first eigenvalue (axis), as well as on the sum of all eigenvalues (ter Braak and Verdonschot, 1995). Ultimately, we used variation partitioning with pCCA to deconstruct total variation in macroinvertebrate dataset into components explained by nutrient and organic pollution (TP, NO3-, NH3, conductivity, DO) and riparian deforestation (canopy openness) predictors (Borcard et al., 1992; Legendre, 2008). By making canonical ordinations constrained by one of the sets of exploratory variables, we were able to disentangle the effects of nutrient and organic loadings and riparian deforestation on species data. To evaluate this variation, we used the following pCCA that excluded the effect of one data matrix: (1) CCA of the species matrix constrained by the nutrient loading matrix; (2) CCA of the species matrix constrained by the deforestation matrix; (3) CCA of the species matrix constrained by the nutrient loading matrix but removing the effect of the deforestation matrix; and (4) CCA of the species matrix constrained by the deforestation matrix but removing the effect of the nutrient loading matrix. With these four constraints of ordination and three data matrices, it was possible to decompose the community variation into the following parts: variation in the macroinvertebrate community that can be explained a) by nutrient loading descriptors independent from deforestation, b) by interaction between nutrient loading and deforestation descriptors, c) by riparian deforestation descriptor independent from nutrient loadings, and d) neither by nutrient loadings nor riparian deforestation data (Borcard et al., 1992; de Azevedo et al., 2007; Legendre, 2008). All multivariate analyses were carried out using RStudio (RStudio, Inc., Boston, MA). Where necessary, data were log10(x) or log10(x + 1) transformed to improve normality and homoscedasticity after exploratory data analysis.

Fig. 2. Linear regression model showing the effect of human population density (HPD) on total phosphorus (TP) loadings.

3. Results 3.1. Environmental parameters

Fig. 3. Linear regression model showing the effect of total phosphorus (TP) on dissolved oxygen (DO).

TP concentrations (Table A1) of stream water showed substantial spatial variation, ranging from less than the detection limit at the most upstream site to 1.50 mg/l in the downstream sector, where many sites scored above the upper limit of hypertrophication (0.30–0.64 mg/l; Cunha et al., 2011; Choi et al., 2015). TP was significantly positively correlated with GIS variables (Table A2), such as human population density (HPD) (r = 0.788, p = 0.001) and fraction of built-up areas (r = 0.598, p = 0.016), but significantly negatively with the fraction of agriculture (r = −0.684, p = 0.005) and the fraction of shrublands and grasslands (r = −0.687, p = 0.005). Stepwise multiple regression analysis revealed that HPD was the most appropriate descriptor of TP loadings (Fig. 2). Spatial variation in NO3- was significantly negatively correlated with HPD (r = −0.559, p = 0.023) and with the fraction of built-up areas (r = −0.497, p = 0.042). Although NH3 concentrations tended to increase with higher HPD values (Fig. A1), their discrete data distribution did not satisfy the requirement for a regression analysis. As an indicator of water quality, DO ranged widely, from 0.13 to 6.27 mg/l, among the studied streams (Table A1), and decreased significantly with increasing TP concentration (r = −0.742; p = 0.002; Fig. 3). A similar correlation was found between DO and NH3, though it could not be fitted to a linear model (Fig. A2).

variables across study streams, we used these species abundance data to produce a pCCA ordination. After removing the possible effect of longitudinal variation, the first two pCCA axes obtained were accounted for 61.7% of the variation in the data set. The correlation coefficients between the environmental variables and the ordination axes (interset correlation) reflect the relative importance of each environmental variable in determining the composition of the benthic macroinvertebrate community. The total inertia in species dispersion in the data set was 2.39, which was highly significant (p < 0.001) using a Monte Carlo permutation test (Table 1). Axis 1 corresponds to all the indicators of nutrient and organic loadings (TP, NO3-, NH3, Table 1 Results of the main ordination for the pCCA. The boldface values were significant (p < 0.05).

3.2. Benthic community response A total of 2,549 stream benthic macroinvertebrates from 36 genera were collected across all sampling sites. They were largely dominated by Diptera (79.6%), followed by Ephemeroptera (10.3%), Coleoptera (1.3%), Trichoptera (1.2%) and Odonata (0.4%). The remaining percentage (7.2%) represented different rare taxa (Table A3). Together with non-collinear and most probable explanatory environmental

Axes 1 2 3 Interset correlation of environmental variables

4

5

6

DO Conductivity NH3 TP NO3Canopy Openness

−0.43 0.20 −0.14 0.08 −0.53 0.18

−0.10 0.05 −0.15 0.02 0.08 0.45

−0.08 0.10 −0.10 −0.45 0.40 −0.13

0.05 −0.15 0.16 −0.28 −0.16 −0.21

0.19 14.0

0.17 12.5

0.10 7.2

0.06 4.6

0.78 −0.96 −0.91 −0.73 0.73 −0.83

−0.44 0.09 0.28 0.42 0.03 −0.10

Summary of the main ordination diagnostics Eigenvalues 0.48 0.35 Explained variation (%) 35.6 26.1 Total Inertia 2.39

4

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Fig. 5. Venn diagram showing the fraction of variation in benthic macroinvertebrates explained by nutrient and organic pollution, riparian deforestation, and the intersection between these two predictors based on variation partitioning applied to pCCA. Fig. 4. Triplot of the first and second pCCA axes of macroinvertebrate taxa, environmental variables (nutrient and organic pollution predictors: TP, NO3-, NH3, conductivity, DO; riparian deforestation predictor: canopy openness) and sampling sites. Taxa from Ephemeroptera: Acentrella sp., Afronurus sp., Caenis spp., Choroterpes sp., Trichoptera: Ceratopsyche sp., Cheumatopsyche sp., Coleoptera: Stenelmis sp., Diptera: Chironomus spp., Clogmia sp., Paracladopelma sp., Polypedilum sp., Thienemannimyia sp., Mesogastropda: Melanoides sp., Thiara spp., Unionoida: Corbicula sp., Basommatophora: Lymnaea sp., Decapoda: Potamidae gen. sp., Arhynchobdellida: Barbronia sp., Opisthopora: Almidae gen. sp.

macroinvertebrates also declined at high NH3 levels, although the linear regression analysis could not be applied here (Fig. A3). Similarly, macroinvertebrate assemblages (e.g. EPT richness and density) under these conditions depauperate while taxa from Oligochaeta and Chironomidae started to occur and become denser, respectively (Table 2). Ultimately, stepwise multiple regression analysis revealed that canopy openness was the best descriptor of diverse functional feeding groups of EPT taxa (i.e. collector-gatherers, shredders, and filterers), as compared to other proxies of physical disturbances (e.g. fractions of land-use types, HPD). Canopy openness had a substantial and significant negative effect on EPT richness (rs = −0.722, p = 0.005; Fig. 8A) and on log EPT density (rs = −0.766, p = 0.002; Fig. 8B) as well as on H' (r = −0.829, p < 0.001; Fig. A4) and on S (r = −0.880, p < 0.001; Fig. A4B).

conductivity, DO) and riparian deforestation (canopy openness), while axis 2 corresponds to the interaction of TP loadings and DO depletion (Table 1). Axis 1 explained 35.6% of the species assemblage based on all the environmental variables used. It separated sites that had higher DO and NO3- and lower conductivity, NH3, TP, and canopy openness (sites 1–7) from those sites with opposite condition (sites 8-13). The former condition was positively associated with the abundance of macroinvertebrate taxa, such as Acentrella sp., Afronurus sp., Caenis spp., Choroterpes sp., Ceratopsyche sp., Cheumatopsyche sp., Stenelmis sp., Paracladopelma sp., Polypedilum sp., Thienemannimyia sp., Melanoides sp. Corbicula sp., Potamidae gen. sp., and Barbronia sp. while the latter condition was tolerated by Chironomus spp., Clogmia sp., Thiara spp., Lymnaea sp. and Almidae gen. sp. (Fig. 4). Furthermore, axis 2 explained 26.1% of the species assemblage according to increase of TP loadings and its concomitant DO reduction. This condition was associated with the abundance of Chironomus spp., Clogmia sp., and Almidae gen. sp. (Fig. 4). Variation portioning through pCCA was successfully used to discriminate the effects of nutrient loadings and deforestation on benthic macroinvertebrates in SSRS. These environmental variables explained 75.2% of the variation among macroinvertebrate communities. Of which, 53.5% (F = 2.6596, p < 0.001) was due to nutrient and organic pollution predictors while 9.7% (F = 3.0459, p < 0.001) was attributed to riparian deforestation. The intersection between the two explanatory variables accounted for 12.0% of observed species matrix. The unexplained variation in this species matrix was 24.8% (Fig. 5).

4. Discussion Our study demonstrates how urbanisation as expressed as increasing HPD and TP loading, which deteriorates stream environments, results in hypoxia and loss of benthic macroinvertebrate diversity. In the watersheds of developing countries, like the SSRS, where wastewater treatment plants (WWTPs) are poorly installed, one of the possible TP sources is P-rich household detergents in domestic wastes (Mokaya et al., 2004; Borbor-Cordova et al., 2006). The two functioning WWTPs located in Silang, Cavite and Biňan, Laguna (capacity: 2,000 m3/day) designated to treat household wastes are insufficient to maintain the water quality of the entire SSRS. This is compounded by the fact that Santa Rosa, Laguna, where most of the urban developments in the catchment are located, does not have appropriate WWTPs. Domestic activities are shown to be the main contributor of TP loadings as indicated by increasing TP concentrations along the HPD and built-up area fraction gradients. In developed countries, in contrast, while replacement of phosphate-containing detergents by other substances and establishing enough WWTPs have successfully reduced TP loadings from domestic discharges, excessive TP may persist; this pattern is then attributed to agricultural activities (Daniel et al., 1998; Gentry et al., 2007; Pitarch et al., 2016). Urban stream waters in the Philippines are heavily polluted by septic wastes and direct discharges from residential and industrial establishments (Dyer et al., 2003). Other studies with similar site conditions corroborate our suggestion that observed high values of TDS, salinity, and conductivity in the SSRS can be well attributed to increased nutrient loads derived from human activities in theses urban areas (Ocon and Capítulo, 2004; Mouri et al., 2011; Arimoro et al., 2015; Tobes et al., 2016). Such organic pollution can cause hypoxia in streams (Daniel et al., 2002; Azrina et al., 2006; Kasangaki et al., 2008; Tobes et al., 2016). In the SSRS, TP concentration was the primary

3.3. Benthic macroinvertebrate diversity The diversity of benthic macroinvertebrates showed a decreasing trend downstream except for two sites near the coast, as measured by both the Shannon–Wiener diversity index (H') and species richness (S) (Table 2, Fig. 6). DO was the most significant determinant of H' (r = 0.731, p = 0.002; Fig. 7A) and S (r = 0.850, p < 0.001; Fig. 7B), but not D (r = −0.128, p = 0.339), confirming that most taxa depend on DO, as shown in the pCCA ordination (Fig. 4). The H' of benthic 5

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–

26.90 ( ± 16.14) – –

7720.30 ( ± 839.90) 306.66 ( ± 231.34) 1721.60 ( ± 452.60) 86.08 ( ± 86.08)

– – – – –

129.12 ( ± 21.52) – 53.80 – –

1 – 53.8 – –

–

–

0.97 ( ± 0.13) 0.76 ( ± 0.24) 4 ( ± 1.00) 193.68 ( ± 61.40) – 0.72 ( ± 0.40) 0.58 ( ± 0.11) 3.5 ( ± 1.50) 177.54 ( ± 69.94) – 0.15 ( ± 0.08) 0.10 ( ± 0.07) 3 (±) 8037.72 ( ± 106.24) – 0.11 ( ± 0.11) 0.20 ( ± 0.10) 2 ( ± 1.00) 1813.06 ( ± 144.06) – 0.23 ( ± 0.23) 0.33 ( ± 0.33) 1.5 ( ± 0.50) 139.88 ( ± 10.76) –

5.5 ( ± 0.50) 295.9 ( ± 48.42) 3 – 258.24 ( ± 43.04) 5.38 ( ± 5.38) –

0.98 – 0.58 ( ± 0.03)

1.67 ( ± 0.07) 0.78 ( ± 0.05) 8.5 ( ± 0.50) 801.62 ( ± 306.66) 1.5 ( ± 0.50) 48.42 ( ± 26.90) 570.28 ( ± 290.52) –

1.45 ( ± 0.33) 0.72 ( ± 0.02) 8 ( ± 3.00) 489.58 ( ± 106.66) 2 – 129.12 – 301.28 ( ± 301.28) –

0.89 ( ± 0.67) 0.52 ( ± 0.19) 5.5 ( ± 3.50) 634.84 ( ± 151.92) 1.5 ( ± 1.50) 355.08 ( ± 355.08) 26.90 ( ± 6.90) –

1.32 ( ± 0.35) 0.68 ( ± 0.08) 7 ( ± 2.00) 419.64 ( ± 118.36) 3 ( ± 1.00) 322.8 ( ± 96.84) 43.04 ( ± 10.76) –

1.69 ( ± 0.02) 0.82 ( ± 0.06) 8 ( ± 1.00) 484.2 ( ± 258.24) 4 ( ± 1.00) 430.4 ( ± 269.00) 21.52 ( ± 10.76) –

–

factor among chemical variables determining stream water DO. The excessive primary productivity driven by P enrichment leads to high rates of decomposition and microbial respiration, resulting in depletion of DO in benthic environments (Correll, 1998; Mallin et al., 2004). NO3- has often been determined as indicator of human activity in catchments (Gergel et al., 2002; Mokaya et al., 2004; Inwood et al., 2005; King et al., 2005). Contrary to our expectations, NO3- concentrations in stream waters of the SSRS decreased rather than increased with increasing HPD and fraction of built-up areas. However, we also found elevated NH3 concentrations when DO was ≤1.29 mg/l (Fig. A2). This threshold may be associated with hypoxia, which inhibits nitrification of NH3 derived from mineralisation of organic nitrogen (Seitzinger, 1988; Bradley et al., 1995; Yang et al., 2009). The hypoxic condition can also enhance denitrification, in which dissolved NO3- is transformed to atmospheric nitrogen, resulting in the removal of N from the stream ecosystems (Mulholland et al., 2008; Beaulieu et al., 2011). This information makes TP, as compared to other nitrogen-based nutrients, an even better indicator of nutrient loadings in urbanised stream systems of developing countries lacking WWTPs. The documented eutrophication in the SSRS of Laguna de Bay underscores the need for comprehensive assessment, monitoring and governance among other watersheds of the lake. This may further improve the status of the lake which has recently showed decreased in bloom intensity due to less warming events (Ho et al., 2019). Considering current and future synergistic effects between urbanisation and climate changes, our understanding on stream eutrophication will be more complicated (Nelson et al., 2009). Streams collect and disperse heat that drives changes throughout the watersheds. Warming in streams is expected to alter temperature and runoff regimes that may, in turn, increase frequency and intensity of algal blooms (Holbach et al., 2013), floods, and droughts (Milly et al., 2005; Alcamo et al., 2007). As such, it also imperative for water quality management efforts to account for the interactions among climate change, anthropogenic impacts, and hydrological conditions when mitigating eutrophication. Benthic macroinvertebrate diversity decreased downstream with increasing HPD and organic pollution. The DO of stream waters was the most critical factor controlling macroinvertebrate diversity. Our pCCA demonstrated that many oligo- and mesotrophic taxa, such as mayfly (Ephemeroptera) and caddisfly (Trichoptera), were abundant in streams with higher DO concentration, as also reported in many other studies (Ogbogu, 2001; Nelson and Lieberman, 2002; Merritt et al., 2008; Arimoro and Ikomi, 2009; Kenney et al., 2009; Selvakumar et al., 2014). In contrast, chironomids and oligochaetes were more abundant at lower DO levels. It is well known that the collected Chironomus spp. or red blood-worms are hypoxia-tolerant because of their intrinsic ability to synthesise haemoglobin, which enables them to accommodate respiration under harsh conditions (Ramírez and Pringle, 2006; Sharma and Chowdhary, 2011; Gimenez and Higuti, 2017). Also, Chironomus spp. have posterior tubules involved in osmoregulation, which allows them to thrive in waters with elevated ion concentrations (Tchakonté et al., 2015; Grazioli et al., 2016). This is a possible reason why this taxon also favoured conditions of high conductivity and NH3 concentration. Deforestation in riparian areas, as indicated by canopy openness in the SSRS, showed negative impacts on EPT indices (Benstead et al., 2003; Benstead and Pringle, 2004). The EPT taxa collected (Fig. 4; Table A3) are from diverse functional feeding groups (Merritt et al., 2008; Ramírez and Gutiérrez-Fonseca, 2014; Dalu et al., 2017), such as collector-gatherers (e.g. Acentrella sp., Afronurus sp., Caenis spp., Choroterpes sp.), shredders (e.g. Dudgeodes sp., Anisocentropus sp., Psilotreta sp.) and filterers (e.g. Ceratopsyche sp., Cheumatopsyche sp.), that depend on allochthonous food sources from riparian forests (Hamid et al., 2011; Doi et al., 2007; Gebrehiwot et al., 2017). Streams with high canopy openness are associated with algal growth under strong light intensity, so the EPT taxa are completely replaced by algal grazers, Thiara spp. and Lymnaea sp., which are regarded as eutrophic

Oligochaeta Density

Chironomidae Density

EPT Density

EPT Richness

D

S

Evenness

1.60 ( ± 0.10) 0.90 ( ± 0.03) 6 ( ± 1.00) 172.16 ( ± 86.08) 1.5 ( ± 0.50) 32.8 ( ± 21.52) 21.52 ( ± 21.52) – H'

10 9 8 7 1

2

3

4

5

6

Site Index

Table 2 Mean values ( ± standard errors) for macroinvertebrate indices and metrics across 13 sites in the Silang-Santa Rosa Subwatershed. See text for explanation of indices.

11

12

13

E.M. Peralta, et al.

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Fig. 6. Distribution and population map of benthic macroinvertebrate taxa found in the Silang-Santa Rosa Subwatershed.

organisms (Groff, 2006; Sturt et al., 2011; Galan et al., 2015; Van Echelpoel et al., 2018). Overall, we have successfully disentangled the covarying effects of nutrient and organic pollution and riparian deforestation on the variation of benthic macroinvertebrate communities in the SSRS. Although both explanatory variables have significant impacts on stream system, nutrient and organic pollution contributed more on the decline of water quality and biodiversity in the SSRS. Nonetheless, we still have factored out the importance of riparian canopy cover in providing allochthonous food sources to the diverse feeding groups of EPT taxa. Our approach to discriminate multiple stressors in Philippine tropical urban streams provides a nuanced understanding of how human population increases and landscape changes due to urbanisation can affect stream biodiversity at the catchment scale. In temperate streams, the use of HPD as a proxy of human activity has been recently established in stream ecology, but this paradigm has not been fully demonstrated in tropical

streams (Olson et al., 2015). These findings strongly suggest that HPD can be a driver of biodiversity loss in tropical stream systems. A DPSIR (Drivers, Pressures, State, Impact, Response) analysis of urban stream systems has recently shown the complexity of problems in urban hydrosystems of the Global South (Wantzen et al., 2019), and shown positive examples of how governance (as a top-down approach) and engagement by the urbanites (in a bottom-up approach) may cooperate to achieve efficient management and restoration interventions. The utilisation of WWTPs in more developed countries has successfully restored nutrient balances and halted additional biodiversity loss (Northington and Hershey, 2006; Bernhardt and Palmer, 2007). In urbanised streams of developing countries, installation of WWTPs is necessary to address similar problems. Considering the huge cost of WWTP construction, however, restoration of riparian forests (Bernhardt and Palmer, 2007) can be a less costly alternative measure for mitigating benthic macroinvertebrates’ environmental stressors in the

Fig. 7. Linear regression showing the relationship between dissolved oxygen (DO) and benthic macroinvertebrate (A) diversity H' and (B) richness S. 7

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Fig. 8. Exponential models for the effects of canopy openness on (A) EPT richness and (B) log EPT density.

urbanised streams of developing countries such as the Philippines. We can also adapt successful river restoration projects of Taiwan and Japan (Shimatani, 2000, 2003; Nakamura et al., 2006; Yang, 2012; Chou, 2016) which both have experienced similar environmental pressures on streams such as land use changes, flooding, and nutrient pollution driven by intense urbanization (Yoshimura et al., 2005; Lin and Yo, 2008; Lin et al., 2008). These past events have encouraged Japan and Taiwan government agencies to mitigate further damage of lotic ecosystems through stream bioassessment protocols and restoration interventions such as de-culverting, floodplain improvement, on-site treatment and sewage-intercepting facilities, and stream artificial widening and sediment augmentation (Nakamura et al., 2006; Lin and Yo, 2008; Chou, 2016). These specific rehabilitation strategies can be considered by the Philippine government aside from their on-going river solid waste clean-up projects which may not be enough in the long run (Gorme et al., 2010; National Economic Development Authority [NEDA], 2011). Also, conservation efforts have already started in the country by establishing a freshwater protected area in the upper river basin of a highly urbanised watershed (i.e. Marikina Watershed) to prevent further stream habitat destruction and biodiversity loss (Department of Environment and Natural Resources [DENR], 2012; Peralta et al., 2019). These initial steps of the government could be strengthened through strict implementation of existing environmental laws and biomonitoring programs, rehabilitation of riparian zones, and adaptation of interventions such as sewage-intercepting and wastewater treatment facilities. We hope that the findings and recommendations of this study provide motivation for environmental management agencies to adopt cost-effective mitigation measures for biodiversity conservation and ecosystem management in urbanised streams, especially in developing countries.

Ellis Mika C. Triño: Investigation. Yoshitoshi Uehara: Investigation, Resources. Takuya Ishida: Investigation. Yuki Kobayashi: Investigation. Chia-Ying Ko: Investigation, Writing - review & editing. Tomoya Iwata: Conceptualization, Methodology, Investigation, Writing - review & editing, Supervision. Adelina S. Borja: Conceptualization, Project administration. Jonathan Carlo A. Briones: Writing - review & editing, Supervision. Rey Donne S. Papa: Conceptualization, Resources, Writing - review & editing, Supervision, Project administration. Francis S. Magbanua: Conceptualization, Methodology, Investigation, Resources, Writing - review & editing, Supervision, Project administration. Noboru Okuda: Conceptualization, Methodology, Investigation, Writing - review & editing, Supervision, Project administration, Funding acquisition. Declaration of Competing Interest None. Acknowledgements We are grateful to Dr. Michael Hupfer and two careful reviewers for providing helpful suggestions on the manuscript. We thank Laguna Lake Development Authority for the permission and assistance to carry out the research. Special thanks to Osbert Leo Privaldos of the Chemistry Department, Laguna Lake Development Authority and Dayanara Bianca S. Bermudez of the School of Urban and Regional Planning, University of the Philippines-Diliman for nutrient and GIS analyses, respectively. We acknowledge the use of the research facilities of the Institute of Biology, University of the Philippines Diliman and the Research Center for the Natural and Applied Sciences, University of Santo Tomas (UST). This research was supported by the Biodiversitydriven Nutrient Cycling and Human Well-being in Social-Ecological Systems ‘e-REC’ Project (D06-14200119) at Research Institute for Humanity and Nature, Kyoto, Japan. E. Peralta is deeply indebted to the UST Office for Grants, Endowments, and Partnerships in Higher Education for granting him the San Martin Scholarship to pursue graduate studies.

CRediT authorship contribution statement Elfritzson M. Peralta: Conceptualization, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Leocris S. Batucan: Investigation, Writing original draft. Irisse Bianca B. De Jesus: Investigation, Visualization.

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Appendix A

Table A1 Mean values ( ± standard errors) of environmental variables for the 13 sites in the Silang-Santa Rosa Subwatershed. Environmental Variables Canopy openness (%) Temperature (°C) TDS (mg/l) Salinity (mg/l) Conductivity (μS/cm) pH DO (mg/l) TP (mg/l) NO3- (mg/l) NH3 (mg/l)

Site 1

2

3

4

5

6

7

8

9

10

11

12

13

28.33 ( ± 0.91) 27.33 ( ± 0.03) 193.33 ( ± 0.33) 131.33 ( ± 0.88) 279.67 ( ± 0.88) 6.88 ( ± 0.01) 3.10 ( ± 0.45) – 2.21 0.02

17.48 ( ± 1.58) 26.33 ( ± 0.28) 228.33 ( ± 1.86) 153.67 ( ± 1.20) 328.33 ( ± 3.33) 6.70 ( ± 0.06) 3.80 ( ± 0.15) 0.67 1.34 –

15.99 ( ± 1.68) 28.50 ( ± 0.12) 247.00 ( ± 2.00) 165.33 ( ± 1.20) 353.67 ( ± 2.19) 7.85 ( ± 0.01) 5.68 ( ± 0.03) 0.96 1.77 0.11

5.66 ( ± 0.52) 25.20 – 217.00 ( ± 1.53) 148.33 ( ± 1.67) 312.33 ( ± 0.88) 8.08 ( ± 0.01) 5.71 ( ± 0.04) 0.53 2.45 0.05

10.20 ( ± 2.62) 25.80 ( ± 0.10) 188.67 ( ± 0.33) 124.67 ( ± 0.33) 266.00 ( ± 4.04) 7.88 ( ± 0.02) 6.27 ( ± 0.22) 0.75 0.57 0.02

17.30 ( ± 1.21) 26.57 ( ± 0.12) 264.33 ( ± 1.67) 177.33 ( ± 1.20) 378.67 ( ± 0.67) 7.21 ( ± 0.01) 4.50 ( ± 0.06) 0.80 1.58 –

30.70 ( ± 2.09) 26.27 ( ± 0.07) 261.33 ( ± 2.19) 175.67 ( ± 1.33) 373.67 ( ± 1.76) 7.28 ( ± 0.01) 4.90 ( ± 0.03) 0.59 1.52 0.01

78.79 ( ± 0.47) 27.90 ( ± 0.12) 599.33 ( ± 8.51) 419.00 ( ± 5.86) 856.33 ( ± 17.85) 7.22 ( ± 0.04) 0.50 ( ± 0.01) 1.24 0.93 2.37

73.98 ( ± 2.52) 30.43 ( ± 0.20) 734.00 ( ± 31.97) 483.33 ( ± 17.34) 993.67 ( ± 54.36) 7.39 ( ± 0.01) 0.85 ( ± 0.06) 1.25 0.85 2.10

73.01 ( ± 1.33) 28.50 ( ± 0.21) 541.00 – 370.67 ( ± 1.45) 776.33 ( ± 0.88) 7.54 ( ± 0.01) 1.29 ( ± 0.08) 1.14 0.10 2.07

78.71 ( ± 2.03) 29.43 ( ± 0.09) 636.33 ( ± 3.18) 438.67 ( ± 4.37) 912.33 ( ± 4.26) 7.09 ( ± 0.02) 1.26 ( ± 0.16) 1.08 0.20 2.21

45.99 ( ± 2.49) 28.77 ( ± 0.07) 704.00 ( ± 15.95) 483.67 ( ± 15.06) 1007.33 ( ± 19.15) 7.42 ( ± 0.03) 0.13 ( ± 0.01) 1.50 – 2.45

63.14 ( ± 3.69) 27.53 ( ± 0.03) 592.33 ( ± 5.04) 408.33 ( ± 0.88) 816.33 ( ± 33.65) 7.59 ( ± 0.02) 0.76 ( ± 0.64) 1.32 – 2.43

TDS, Total Dissolved Solids; DO, Dissolved Oxygen; TP, Total Phosphorus; NO3-, Nitrates; NH3, Ammonia. Table A2 Geographic information system (GIS) variables representing human activities in the 13 study sites in the Silang-Santa Rosa Subwatershed. GIS Data

Site

2

Log HPD (population/km ) Fraction of agricultural area Fraction of built-up area Fraction of shrublands and grasslands

1

2

3

4

5

6

7

8

9

10

11

12

13

4.02 1.73 0.27 0.98

4.28 1.42 1.27 0.45

4.44 0.38 2.47 0.09

4.71 0.31 1.73 0.88

4.72 0.54 1.73 0.76

4.54 1.63 0.40 0.00

4.78 1.56 0.45 0.03

6.42 0.34 1.80 0.34

6.45 0.43 1.66 0.34

5.08 0.00 2.63 0.00

5.25 0.05 2.54 0.00

5.82 0.54 1.71 0.00

5.38 0.16 1.55 0.00

HPD, Human Population Density.

Table A3 Variation in total benthic macroinvertebrates abundance across 13 sites in Silang-Santa Rosa Subwatershed, the Philippines. Taxa

Abundance 1

Ephemeroptera Baetidae Acentrella sp. Caenidae Caenis spp. Heptageniidae Afronurus sp. Leptophlebiidae Choroterpes sp. Teloganodidae Dudgeodes sp. Trichoptera Calamoceratidae Anisocentropus sp. Goeridae Goera sp. Odontoceridae Psilotreta sp. Hydropsychidae Ceratopsyche sp. Cheumatopsyche sp. Coleoptera Elmidae Stenelmis sp.

2

40

3

4

1

1

6

22

2 1

5

6

7

8

9

10

11

12

13

2 48

50

20

3

1

12

15

7

30

2 3 3 3 1

11 7

2 2

3

20

5

1

(continued on next page) 9

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Table A3 (continued) Taxa

Abundance 1

Staphylinidae gen. sp. Diptera Athericidae Atherix sp. Chironomidae Chironomus spp. Polypedilum sp. Paracladopelma sp. Thienemannimyia sp. Robackia sp. Culicidae Topomyia sp. Ephydridae gen. sp. Psychodidae Clogmia sp. Sarcophagidae Fletcherimyia sp. Syrphidae gen. sp. Tipulidae Hexatoma sp. Odonata Corduliidae Epitheca sp. Decapoda Potamidae gen. sp Neotaeniglossa Thiaridae Thiara spp. Melanoides spp. Veneroida Corbiculidae Corbicula fluminea Hygrophila Lymnaeidae Lymnaea sp. Planorbidae Indoplanorbis sp. Rhynchobdellida Glossiphonidae Helobdella sp. Arynchodellida Erpobdellidae Barbronia sp. Phyllodocida Nereididae Namanereis sp. Opisthopora Almidae gen. sp. Lepidoptera Crambidae gen. sp.

2

3

4

5

6

7

8

9

10

11

12

13

1 1 4

1

1 22 36 44 4

8 14 29 4 1

7 5

4

10

24

320

1,435

5

1 2

1

2

1

10

1 25 3 1

1

1

11 1

4

2

2

22

2

2

3

1 1 3

23

1

1

12

1

1

1

8

2 1 5

7

2 2 16 1

Fig. A1. Correlation between human population density (HPD) and NH3. 10

57

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Fig. A2. Correlation between NH3 and dissolved oxygen (DO).

Fig. A3. Correlation between NH3 and diversity H'.

Fig. A4. Correlation between canopy openness and benthic macroinvertebrate (A) diversity H' and (B) richness S.

Environment Federation. Arimoro, F.O., Ikomi, R.B., 2009. Ecological integrity of upper Warri River, Niger Delta using aquatic insects as bioindicators. Ecol. Indic. 9, 455–461. Arimoro, F.O., Odume, O.N., Uhunoma, S.I., Edegbene, A.O., 2015. Anthropogenic impact on water chemistry and benthic macroinvertebrate associated changes in a southern Nigeria stream. Environ. Monit. Assess. 187, 1–14. Austin, K.G., González-Roglich, M., Schaffer-Smith, D., Schwantes, A.M., Swenson, J.J., 2017. Trends in size of tropical deforestation events signal increasing dominance of industrial-scale drivers. Environ. Res. Lett. 12, 1–10. Azrina, M.Z., Yap, C.K., Ismail, A.R., Ismail, A., Tan, S.G., 2006. Anthropogenic impacts

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