Stream benthic macroinvertebrate assemblagesreveal the importance of a recently established

freshwater protected area in a tropical watershed

By Elfritzson M. Peralta*, Alexis E. Belen, Gelsie Rose Buenaventura, FrancisGodwin G. Cantre, Katharine Grace R. Espiritu, Jana Nicole A. De Vera, Cristine

P. Perez, Aleziz Kryzzien V. Tan, Irisse Bianca B. De Jesus, Paul Palomares,Jonathan Carlo A. Briones, Tohru Ikeya, Francis S. Magbanua, Rey Donne S.

Papa, and Noboru Okuda

AbstractUnsustainable urbanization in the Indo-Pacific continues to threaten terrestrial and aquatic ecosystems due to habitat disturbances driven by human pressures. The Marikina Watershed, oneof the most critical watersheds in the Philippines, has been exposed to economic and population growth resulting in landscape modification and water quality degradation. This led to establishment of the Upper Marikina River Basin Protected Landscape (UMRBPL) to rehabilitate the watershed ecosystem. To strengthen this conservation initiative, we aimed to assess whether the establishment of UMRBPL has been effective in conservation of benthic macroinvertebrate diversity in streams of the Marikina Watershed. Sixteen streams, eight from UMRBPL and eight from adjoining unprotected areas, were monitored for benthic macroinvertebrate assemblages and their habitat environments, such as pH, water temperature, dissolved oxygen, total dissolved solids, conductivity, salinity, and canopy openness. Principal component analysis and non-metric multidimensional scaling based on the environmental variables and biological metrics, respectively, revealed that habitat quality and benthic macroinvertebrate assemblages significantly differed between the protected and unprotected streams, with the former having better environment and higher biodiversity. More precisely, protected streams have significantly higher dissolved oxygen and lower canopy openness and material loadings as compared to unprotected streams. Consequently, taxon richness was four-fold higher in protected streams while stream quality indices based on abundance of key invertebrate groups (EPT and EPTC) were ten-fold higher in protected streams, as compared to unprotected streams. This study demonstrates that freshwater protected areas play crucial roles inthe conservation of stream ecosystems and biodiversity under rapid urbanization in developing countries, like the Philippines.*Corresponding Author E-mail: elfritzsonperalta@gmail.com

Pacific Science, vol. 73, no. 3May 17, 2019 (Early view)

Introduction

Urbanization in the Indo-Pacific imposes significant demands on aquatic environments in terms

of water resource uses (Cocklin and Keen 2000). As a result, urban systems continue to threaten

stream biodiversity and ecosystem functions as they are heavily exposed to human disturbances,

such as water supplies, irrigation, power generation, and waste disposal, leading to scarcity of

biological resources and ecological disruption of habitat networks in the watershed (Malmqvist

and Rundle 2002; Gleick and Palaniappan 2010; Ramírez et al. 2012; Hassall and Anderson

2015). Increasing human pressure due to urbanization leads to profound ecosystem alterations

which include considerable changes in biological, chemical, and physical components of stream

ecosystems and now commonly termed as “urban stream syndrome” (Walsh et al. 2005; Booth et

al. 2016). Unfavorable effects of this syndrome are common around the world, but the

mechanism and extent of ecological responses to urban pressures vary from place to place (Utz

et al. 2016). This can be manifested through changes in physicochemical factors such as pH,

dissolved oxygen, total dissolved solids, and conductivity, among others (Bryant and Carlisle

2012). Such environmental changes affect aquatic organisms that inhabit the freshwater streams.

In stream communities, benthic macroinvertebrates are among the most responsive to changes in

physical and chemical properties of streams (Gonzalo and Camargo 2013; Tonkin 2014; Bertaso

et al. 2015) brought about by anthropogenic stressors (Cairns and Pratt 1993; Rosenberg and

Resh 1993; Karlen et al. 2010; Chadwick et al. 2011; Lakew and Moog 2015). These taxa are

ideal bioindicators as they are ubiquitous and show compounded environmental effects over

time; unlike chemical tests which only show conditions for a single point in time or measuring

stream fishes which are often elusive and difficult to catch (Kenney et al. 2009; Uherek and

Pinto Gouveia 2014; Ruaro et al. 2016).

To mitigate the stressors, great efforts have been dedicated to establishment of protected

areas worldwide since 1765 when the oldest legally protected reserve, Main Ridge Forest

Reserve in Trinidad and Tobago, was established (Ramdial 1980; Maunder et al. 2008).

Protected areas are globally recognized as the most important conservation tool as this ensures

protection of habitats against destructive uses and hence prevention of biodiversity loss

(Rodrigues et al. 2004a,b). In spite of sensitivity and fragility, however, few protected areas have

been established for freshwater systems. Instead, freshwater habitats are usually protected only

incidentally as a part of their inclusion within terrestrial reserves which does not guarantee

protection. Notably, protection initiatives for these areas are still slow relative to those for marine

systems (Saunders et al. 2002; Kingsford and Nevill 2005; Suski and Cooke 2006). With the fact

that freshwater species have experienced substantial population declines since 1970’s (McLellan

et al. 2014), the need for paradigm shift, improved framework, and expansion for freshwater

protected areas have been identified to effectively aid in conserving freshwater ecosystems and

biodiversity (Saunders et al. 2002; Kingsford and Nevill 2005; Suski and Cooke 2006; Abell et

al. 2007; Nel et al. 2007; Roux et al. 2008; Pittock et al. 2008; Kingsford et al. 2011). In order to

prevent rapid and global biodiversity losses, the Convention on Biological Diversity set the Aichi

Target 11 which aims to expand the global protection coverage of terrestrial and inland water

areas by at least 17% and marine areas by at least 10% to be met by 2020 (CBD 2010; Venter et

al. 2014; Watson et al. 2014).

Here, we assessed whether establishment of protected areas has been effective in

biodiversity conservation in the Marikina Watershed located at 30 km northeast of highly

urbanized Metro Manila. In this watershed, land conversion due to rapid urbanization has led to

deforestation and siltation, which might have spoiled the integrity of stream ecosystems (JICA

1994; Abino et al. 2015). Considering such anthropogenic impacts, the Upper Marikina River

Basin Protected Landscape (UMRBPL) was established in 2011 to ensure protection of upstream

sections in the watershed where diverse native plant and animal species inhabit (Mancini et al.

2005; DENR 2012; DENR 2015). However, biodiversity assessment has been poorly conducted

in the UMRBPL despite its “protected area” status. The lack of baseline ecological information

in this freshwater protected area is compounded by the fact that human pressures continue to

creep in the reserve through illegal settlements and deforestation (DENR 2015). For plan–do–

check–act (PDCA) cycles to be implemented (Miles 2008), it is necessary to examine if the

recently established freshwater protected area has positive effects on aquatic environments and

biodiversity. For this purpose, we aimed to conduct ecosystem assessment in the Marikina

Watershed, in which monitoring was designed to compare stream environments and benthic

macroinvertebrate assemblages between the UMRBPL and the adjoining unprotected areas.

Materials and Methods

Study area

The Marikina Watershed, one of the major stream networks of the Laguna de Bay, is located at

the northeastern part of the province of Rizal with the geographic coordinates of 14°50’ to

14°34’ North Latitude and 121°20’ East Longitude with a total area of 698.27 km2. It has a Type

I climate with two pronounced seasons which is dry from November to April and wet during the

rest of the year. High rainfall occurs during the months of June to September with an annual

average of 2,574 mm. This watershed used to supply water for industrial, agricultural and

domestic uses after construction of the Wawa Dam in 1909. (JICA 1994; DENR 2015; Corporal-

Lodangco and Leslie 2017; Hilario et al. 2017). In 2010, the total human population in the entire

basin reached 7.5 million, which is 8.1% of the total national population. To date, the built-up

areas within the watershed are mostly composed of residential, commercial and industrial land

uses to accommodate rapid population growth and economic developments. To protect the upper

portions of the basin from further urban pressures, the UMRBPL was established inside the

watershed located at 14°40′16″N and 121°12′50″E with a total area of 261.26 km2 covering

37.4% of the whole catchment area, more than two-fold of the Aichi Target set at 17% for inland

waters. Human activities such as tourism, informal settlements, and deforestation continue inside

this freshwater protected area (CBD 2010; DENR 2015). Considering these scenarios, sixteen

sampling sites were assigned to 8 protected and 8 unprotected streams within the entire

watershed. The protected streams are located within the UMRBPL, whereas the unprotected

streams are located adjacent to the UMRBPL (Fig. 1; Supplemental online material Table S1).

<<Figure 1 near here>>

Environmental variables

At three points of each stream site, environmental parameters, conductivity, salinity, pH,

temperature, and total dissolved solids (TDS), were measured with a hand-held multiparameter

probe (EC500; Extech Instruments, NH, USA), while dissolved oxygen (DO) was measured with

a DO meter (DO600; Extech Instruments, NH, USA). The conductivity, salinity and TDS are

regarded as indicators of material loadings. Also, water velocity was determined at each

monitoring point using a flow meter (fabricated at Research Institute for Humanity and Nature,

Kyoto, Japan). Canopy openness, which serves as a good proxy of riparian vegetation cover, was

determined by taking digital photos (NIKON D7000, Japan) with a fish eye lens (Sigma 4.5mm

F2.8 EX DC Circular Fisheye HSN, Japan), and the digital images were analyzed using

CanopOn 2.0 software (http://takenaka-akio.org/etc/canopon2).

Benthic macroinvertebrate assemblages

Sampling of benthic macroinvertebrates was conducted from October 2016 to November 2016,

during which climates shifted from the wet to dry seasons. Using a Surber sampler (30cm x

30cm; 500µm mesh), the macroinvertebrate samples were collected from two randomly selected

points at each stream and stored in a cooler. Within 24-48 hours, samples were sorted and stored

in scintillation vials with 95% ethanol.

The specimens were identified under stereo (BS-3044, Bestscope, China; SMZ 171,

Motic Asia, Hong Kong) and compound (BA310, Motic Asia, Hong Kong) microscopes down to

genus level using appropriate taxonomic keys of Pescador et al. (1995), Epler (1996), Dudgeon

(1999), Yule and Yong (2004), Merritt et al. (2008), Sartori et al. (2008), Madden (2009), and

Bae (2010).

Data analyses

Mean values (triplicate measurements per site) of all environmental variables were subjected to

t-test and principal component analysis (PCA) to describe the variation between protected and

unprotected streams. Prior to PCA, conductivity was selected among highly multicollinear

(Pearson correlation r >0.80, P <0.001) parameters such as conductivity, salinity and TDS. The

latter two parameters were excluded from the analysis to avoid redundant explanatory variables.

Principle components (PCs) with eigenvalues >1 were retained for interpretation. The PC

loadings >|0.60| were considered qualitatively high. Factor scores from these loadings were

incorporated as variables to compare environmental characteristics between protected and

unprotected streams. These analyses were carried out using IBM SPSS Statistics 20.0 (IBM

Corp., New York, USA).

Using abundance data of all the taxa identified, the taxonomic composition of stream

benthic macroinvertebrate assemblages was compared between protected and unprotected

streams through non-metric multidimensional scaling (NMDS) ordination technique.

Furthermore, we used permutational multivariate analysis of variance (PERMANOVA) to test

for a statistical difference between these two types of streams (Anderson 2001; Anderson et al.

2008). Prior to displaying sample patterns by NMDS and PERMANOVA, abundance data were

analyzed after fourth root transformation and using the Bray-Curtis dissimilarity index as a

resemblance measure to capture important taxa assemblage matrix or relationships. This measure

follows the principle of complementarity, where its maximum score (100) indicates that two

samples have no species in common, irrespective of the precise abundance (Clarke et al. 2006).

Significant terms were investigated with 999 permutations and a significance level of α ≤ 0.05.

These analyses were performed using the software PRIMER 6 (version 6.1.16) and

PERMANOVA+ (version 1.0.6) (Primer-E Ltd, Plymouth, UK).

To further assess biodiversity of local benthic macroinvertebrate assemblages, diversity

indices (Shannon-Wiener diversity and evenness), species richness (S), and density were

computed and subjected to t-tests. The abundance data on Ephemeroptera (E), Plecoptera (P),

Trichoptera (T), and Coleoptera (C) were used to calculate EPT and EPTC indices as

bioindicators. Where necessary, data were log10(x) or log10(x + 1) transformed to improve

normality and homoscedasticity after exploratory data analysis.

Results

Physico-chemical environments

Physico-chemical environmental variables, canopy openness, DO, conductivity, TDS, and

salinity, were significantly different between protected and unprotected streams (Table 1). On

one hand, dissolved oxygen in protected streams was higher than unprotected streams while an

opposite pattern was observed for canopy openness, conductivity, TDS, and salinity. On the

other hand, pH, temperature, and flow velocity values were not significantly different between

two types of streams (Table 1).

<< Table 1 near here>>

Principal components analysis produced three PCs with eigenvalues >1, which accounted for

82.31% of cumulative variance in the environmental variables. Considering that variables with

high absolute values (>0.60) of loadings substantially contribute to the PCs, variables loaded on

PC1 were canopy openness, pH, and conductivity, while those on PC2 were DO and temperature

(Table 2). PCA ordination of these two PCs revealed that PC1 can markedly discriminate

between protected and unprotected streams (Fig. 2).

<< Table 2 near here>>

<< Figure 2 near here>

Benthic macroinvertebrate assemblages

A total of 3,127 stream benthic macroinvertebrates from 79 genera were collected from all 16

stream sites dominated by orders Diptera (34.0%), Ephemeroptera (30.2%), Trichoptera (15.2%),

Coleoptera (13.4%), and Plecoptera (2.6%) with other rare taxa (Odonata, Hemiptera,

Phyllodocida, Rhynchobdellida, Arhynchobdellida, Lepidoptera, Megastropoda, Unionoida,

Basommatophora, Nematomorpha, and Neotaenioglossa) comprising 4.6% of the whole

community (Supplemental online material Table S2). The most abundant genus was Chironomus

spp. with 412 individuals, most of which were collected from an unprotected stream (U8). Two-

dimensional NMDS with PERMANOVA showed a significant difference in community

composition between the protected and unprotected streams (Fig. 3; Pseudo-F = 4.71, P =

0.001).

<< Figure 3 near here>>

Biodiversity assessment in the Marikina Watershed showed that protected areas had positive

effects on diversity of benthic macroinvertebrate assemblages in streams (Table 3). The diversity

index (H’) and evenness were markedly higher in protected streams than unprotected streams.

The protected streams had not only a higher taxon richness, i.e., the number of taxa per unit area,

but also a higher taxon density, i.e., the number of individuals for each community assemblage.

Taxon richness in protected streams averaged 16.3 and was four times greater than unprotected

streams. Strikingly, EPT and EPTC indices which are often used as bioindicators for good water

quality were all ten-fold higher in the protected streams than in the unprotected streams.

<< Table 3 near here>>

Discussion

In this study, we demonstrated that the UMRBPL in the Marikina Watershed has reduced

material loadings indicated by conductivity, TDS and salinity in comparison to unprotected

streams, as was also reported by other studies in which disturbed and undisturbed streams have

been compared (Ometo et al. 2000, Azrina et al. 2006, Kasangaki et al. 2008). Disturbed streams

often contain high amounts of dissolved and suspended materials from agricultural runoff and

urban sewage effluents, resulting in elevated nutrient concentrations and conductivity (Ometo et

al. 2000, Daniel et al. 2002, Azrina et al. 2006, Couceiro et al. 2007; Kasangaki et al. 2008,

Arimoro et al. 2015).

Heavy nutrient loadings enhance in-stream microbial respiration and consequently cause oxygen

consumption, leading to the decrease in DO of stream waters (Gulis and Suberkropp 2003;

Mallin et al. 2006; Wakelin et al. 2008; de Carvalho Aguiar et al. 2011). In some unprotected

streams of the Marikina Watershed, DO levels were too low to sustain populations of hypoxia-

sensitive macroinvertebrates. Such oxygen depletion might be attributed to domestic and

agricultural nutrient loadings from built-up and cropland areas located in the catchments of

unprotected streams (Fig. 1). As detected by our comparative approach, protected areas had

significantly higher stream DO up to the saturation level.

Nutrient pollution and oxygen depletion have negative impacts on stream biota, especially on

benthic macroinvertebrates (Iwata et al. 2003; Ocon and Capítulo 2004; Zhang et al. 2010; Jun et

al. 2011; Arimoro et al. 2015; Wilkins et al. 2015; Tobes et al. 2016). Although the protected

streams were further characterized with lower canopy openness, this did not primarily correlate

with changes in water temperature. Our temperature data fall within the minimum temperature

regimes in Marikina Watershed which is 25.7–27.7°C (DENR 2015). Consistent with the

findings of Rutherford (2004), riparian shade has little to no effect on minimum water

temperature unlike on maximum values. Higher riparian canopy cover in these protected streams,

on the other hand, may contribute an allochthonous food source to be utilized by EPTC taxa

comprised of diverse functional feeding groups such as shredders, gatherers, and filterers (Doi et

al. 2007; Abdul Hamid and Md Rawi 2011).

Our assessment revealed that protected streams have higher biodiversity of benthic

macroinvertebrates than unprotected streams, which are more exposed to human disturbances.

Furthermore, EPT and EPTC indices which are commonly used in stream biomonitoring and

assessments in the tropics and Asia-Pacific successfully discriminated these two types of streams

based on protection status (Walsh et al. 2002; Beauchard et al. 2003; Khanal and Moog 2003;

Boonsoong et al. 2009; Huang et al 2010; Wang et al. 2012; Corbi et al. 2013; Braun et al. 2014;

Tchakonte´ et al. 2015). Consistent with these results, many researches have reported that

freshwater protected areas serve as a sanctuary for aquatic organisms (Williams 1991; Lyle and

Maitland 1992; Keith 2000; Crivelli 2002; Cowx 2002; Saunders et al. 2002; Abellán et al.

2007). Such a general pattern in the freshwater ecosystems also accords with results from the

global meta-analysis for terrestrial ecosystems by Gray et al. (2016), who demonstrated that

terrestrial biodiversity is significantly higher in protected areas than in unprotected areas.

In streams, the benthic macroinvertebrates, which occupy diverse trophic niches in food

webs, mediate trophic energy transfer from primary producers, such as microalgae, macrophytes

and terrestrial plants, to top predators, such as fish and birds (Merritt et al. 1984; Morse et al.

1997; deMoor and Ivanov 2007; Fochetti and Figueroa 2008; Pond 2012), linking between

terrestrial and aquatic ecosystems (Nakano et al. 1999; Baxter et al. 2005). Considering that

biodiversity can provide a diversity of ecosystem functions and services which ultimately

contribute to enhancement of human well-being (Chapin III et al. 2000; Díaz et al. 2006;

Cardinale et al. 2012), the establishment of protected areas has social-ecological consequences

for watershed systems, especially for ones impacted by urbanization. In the case of UMRBPL,

eco-tourism has found its way to the pristine streams and indigenous people utilize the goods and

services in this freshwater protected area (e.g. fishing, bathing, water resource) (DENR 2015).

The present study provided clear evidence on the utility and advantage of freshwater

protected areas in terms of biodiversity conservation in a highly urbanized watershed. In reality,

however, it may be difficult to expand the protected area in the watersheds in which urban

development has already progressed. Considering conflicts among stakeholders for living and

livelihood space, we have to adapt alternative mitigation measures, such as installation of sewage

waste treatment plants and introduction of economic incentives to conservation activities, which

can supplement the establishment of protected areas. For adaptive ecosystem management to be

implemented, cost-effectiveness should be assessed and compared among these alternative

measures through field manipulation and monitoring.

Moreover, protected areas are often influenced by environmental changes in the adjacent

unprotected areas under disturbances (Laurance et al. 2012). In such cases, careful consideration

of where to distribute the protected areas is crucial to successful ecosystem management.

Promising protection schemes should be designed to maximize not just community-scale

diversity (alpha diversity) inside the reserve but also landscape scale diversity (gamma diversity)

across the whole watershed (Whittaker 1960; Parks and Harcourt 2002; Wiersma and Urban

2005; Fabricius et al. 2003). Such criteria will be more pragmatic when protected areas are

established in watersheds with larger catchment size (Roux et al. 2008). In the case of the

Marikina Watershed, conservation efforts have paid attention to its headwaters. According to the

river continuum concept (Vannote et al. 1980), we expect that riparian vegetation in the

protected headwaters can subsidize diverse functional feeding groups of benthic

macroinvertebrates, such as shredders and collector-gathers, in the downstream through

downward transportation of terrestrial materials as energy sources (Saunders et al. 2002). In this

watershed, synoptic monitoring is thus needed to design the best protection scheme to maximize

gamma diversity.

To date, the protection management and law enforcement in UMRBPL are still marred by

the issues and problems of illegal land titling, forest degradation (logging and slash-and-burn),

charcoal making, and small-scale quarrying. The country’s Department of Environment and

Natural Resources has recently identified two major strategies with specific interventions that

will address concerns related to the management of the entire catchment. Generally, this includes

a) strengthening the capabilities of local and national stakeholders to strictly implement

administration policies and conservation measures and b) improvement of ecological (protective)

and socio-economic (productive) values of the watershed (DENR 2015).

In conclusion, the present study demonstrated that the establishment of a freshwater

protected area is effective in environmental and biodiversity conservation in the heavily

impacted stream ecosystem amidst the rapid economic and population growth in Marikina

Watershed. Globally, there will be about 2 billion people living in urban areas, especially of

developing countries, by 2030 but future impacts of ongoing urbanization on biodiversity in the

protected areas are still poorly understood (Mcdonald et al. 2008). Changing climates may also

make our projection of the future impacts more complicated because they can have catastrophic

effects on stream ecosystems (Nelson et al. 2009; Soares-Filho et al. 2010; Hopkin et al. 2015).

Considering synergy effects between urbanization and climate changes, increased efforts for

continuous monitoring are needed to understand the long-term effects of protected areas on the

urbanized watershed ecosystems.

Acknowledgments

We would like to thank Laguna Lake Development Authority and Protected Area Management

Board of the Upper Marikina River Basin Protected Landscape for permission to carry out the

research. We also acknowledge the use of the research facilities of the Institute of Biology,

University of the Philippines Diliman and Research Center for the Natural and Applied Sciences,

University of Santo Tomas (UST). 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. This research was supported by the Research Institute for Humanity

and Nature (RIHN) Project (D06-14200119). We are grateful to Dr. Curtis C. Daehler and the

two anonymous reviewers whose suggestions improved the manuscript.

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ParameterProtected streams Unprotected streams t df P-value

P1 P2 P3 P4 P5 P6 P7 P8GrandMean

U1 U2 U3 U4 U5 U6 U7 U8GrandMean

CanopyOpenness (%)

44.0(9.1)

30.6(4.1)

34.9(2.0)

45.0(0.3)

23.2(2.2)

43.4(1.0)

26.7(4.1)

14.5(5.3)

32.8(3.9)

48.6(1.5)

24.5(4.5)

42.4(5.3)

52.1(2.5)

70.6(0.8)

78.7–

81.2(0.4)

64.7(4.5)

57.8(6.9)

-3.17 14 <0.01

DO(mg L-1)

7.4–

8.9(0.1)

8.1(0.2)

8.3–

7.8(0.2)

7.7(0.2)

7.5(0.1)

6.9(0.1)

7.8(0.2)

5.2–

7.1(0.6)

4.4(1.2)

5.7(0.1)

8.1(0.1)

5.4(0.4)

5.8 (0.1)3.2(0.6)

5.6(0.5)

3.89 14 <0.01

pH7.9(0.4)

8.0(0.2)

7.1–

7.3(0.2)

7.0(0.1)

7.8(0.1)

5.6(0.3)

4.9(0.1)

6.4(0.7)

7.8(0.1)

7.54–

7.5–

7.2(0.2)

8.0(0.1)

6.6(0.2)

7.9 (0.1)7.1–

7.5(0.2)

-1.39 14 0.19

Conductivity(µS cm-1)

274.0(0.6)

349.0–

256.3(0.7)

235.3(11.2)

266.0(2.1)

176.2(3.6)

91.6(1.5)

167.1(1.6)

227.0(28.0)

352.3(5.2)

455.00–

432.3(0.3)

217.3(2.4)

381.7(2.0)

324.3(3.2)

485.7(10.1)

294.0(7.5)

367.8(31.6)

-3.33 14 <0.01

TDS(mg L-1)

189.3(0.7)

242.0–

177.7(1.3)

165.0(10.0)

184.3(0.9)

121.5(3.5)

62.8(0.6)

116.2(1.3)

157.4(19.5)

218.0(15.7)

315.7(0.7)

301.7(0.7)

152.0(2.5)

263.7(1.2)

224.0(2.5)

336.3(8.7)

202.0(5.0)

251.7(22.4)

-3.17 14 <0.01

Temperature(oC)

24.2(0.3)

26.6(0.2)

24.5(0.1)

23.6(0.2)

24.0–

29.4(0.4)

24.0(0.1)

25.8(0.1)

25.3(0.7)

27.3(0.2)

27.6(0.3)

28.5–

26.6(0.2)

25.7(0.2)

27.2(0.1)

24.4(0.2)

28.7(0.1)

27.0(0.5)

-2.04 14 0.06

Salinity(mg L-1)

124.3(3.2)

163.3(1.5)

119.0(0.6)

115.3(0.3)

122.0–

92.4(5.7)

41.6(1.0)

80.2(1.7)

107.3(12.8)

152.3(5.2)

210.3(0.3)

199.0(0.6)

87.9(8.6)

174.0(1.2)

144.3(1.9)

220.3(7.4)

134.0(3.5)

165.3(15.7)

-2.86 14 0.01

Flow velocity(cm sec-1)

60.4(5.0)

81.8(9.3)

64.8(14.1)

71.7(1.3)

39.8(4.7)

90.4(27.2)

43.9(25.6)

45.5(18.6)

62.3(6.5)

45.0(9.9)

19.4(3.3)

45.5(12.6)

105.0(33.6)

55.7(8.9)

32.1(13.7)

33.6(7.6)

76.9(11.4)

51.7(9.8)

0.91 14 0.38

Table 1 Mean (± standard error) values of environmental variables for protected and unprotected streams in the Marikina Watershed– No standard errors

Table 2 Result of principal component analysis with loading factors of environmental variables Environmental variable PC1 PC2 PC3% variation explained 40.00 22.43 19.87

Canopy Openness (%) 0.66 0.37 -0.03

DO (mg L-1) -0.10 -0.91 0.19

pH 0.93 -0.02 0.24

Conductivity (µS cm-1) 0.80 0.15 -0.49

Temperature (oC) 0.16 0.80 0.23

Flow velocity (cm sec-1) 0.02 0.04 0.98

Bold values were considered high (>|0.60|)

Table 3 Mean (± standard error) values of macroinvertebrate diversity indices for protected and unprotected streams of the Marikina Watershed

Response variable

Protected streams Unprotected streams t df P-value

P1 P2 P3 P4 55 P6 P7 P8GrandMean

U1 U2 U3 U4 U5 U6 U7 U8GrandMean

Diversity index (H’)

1.93(0.23)

1.87(0.08)

2.37(0.01)

2.42(0.03)

2.14(0.08)

1.86(0.05)

1.85(0.07)

1.65(0.28)

2.01(0.10)

1.63(0.12)

– 0.72(0.51)

– 1.29(0.18)

0.54(0.54)

– 0.74(0.05)

0.62(0.22)

5.86 14 <0.01

Evenness0.76

(0.54)0.75

(0.53)0.72

(0.51)0.80

(0.56)0.68

(0.48)0.80

(0.56)0.72

(0.51)0.70

(0.50)0.74

(0.01)0.81

(0.57)–

0.48(0.34)

–0.61

(0.43)– –

0.81(0.45)

0.32(0.12

3.42 14 <0.01

Taxon richness

13.00(2.00)

12.00(1.00)

27.00–

21.00(1.00)

23.50(1.50)

10.50(1.50)

13.00(1.00)

10.50(2.50)

16.31(2.30)

7.50(0.50)

1.00–

4.00(1.00)

1.00–

8.50(0.50)

2.50(1.50)

0.50(0.50)

9.50(7.50)

4.31(1.30)

4.54 14 <0.01

Taxon density

919.98(618.70)

817.76(322.80

)

2254.22(59.18)

2205.8(613.32

)

2668.48(365.84)

1484.88(96.84)

1780.78(672.50

)

634.84(96.84)

1595.84(266.87

)

376.60(150.64

)

43.04(32.28)

322.8(161.40

)

53.8(10.76)

624.08(96.84)

59.18(48.42)

16.14(16.14)

2560.88(2442.52)

507.07(303.11)

2.70 14 0.02

EPT richness

7.50(0.50)

6.00(2.00)

12.00 23.50(0.50)

14.50(0.50)

7.50(0.50)

5.50(0.50)

4.00(2.00)

10.06(2.28)

3.00–

–1.00 0.50

(0.50)2.50

(0.50)1.00 0.50

(0.50)2.50

(0.50)1.38

(0.40)3.75 14 <0.01

EPT density

511.1(295.90)

360.46(188.30

)

1441.84(129.12)

1495.64(139.88

)

1705.46(349.70)

817.76(64.56)

645.6(129.12

)

381.98(69.94)

919.98(192.55

)

220.58(123.74

)–

252.86(209.82

)

21.52(21.52)

59.18(16.14)

16.14(5.38)

16.14(16.14)

118.36–

88.10(35.10)

4.25 14 <0.01

EPTC richness

10.00(2.00)

8.50(3.50)

17.50(0.50)

18.00(1.00)

19.50(0.50)

8.50(0.50)

10.50(0.50)

8.00(3.00)

12.56(1.73)

3.00–

–1.00 0.50

(0.50)3.50

(0.50)2.00

(1.00)0.50

(0.50)3.00

(1.00)1.69

(0.48)6.07 14 <0.01

EPTC density

543.38(317.42)

451.92(247.48

)

1592.48(182.92)

1861.48(430.40

)

2248.84(161.40)

871.56(32.28)

1511.78(500.34

)

484.2(75.32)

1195.71(246.39

)

220.58(123.74

)–

252.86(209.82

)

21.52(21.52)

86.08(10.76)

26.90(16.14)

16.14(16.14)

123.74(5.38)

93.48(34.55)

4.43 14 <0.01

– No computed values and/or standard error

Fig. 1 Map of study sites (protected: 8, unprotected: 8) in the Marikina Watershed with land cover patterns.

Fig. 2 PCA bi-plot based on the major two PC scores across 16 streams in the Marikina Watershed.

Fig. 3 Two-dimensional NMDS ordination based on abundance of benthic macroinvertebrate taxa using Bray-Curtis dissimilarity index.

Supplementary Table S1. Status of the study stream reaches in Marikina Watershed.

Protected streams Unprotected streamsP1 P2 P3 P4 P5 P6 P7 P8 U1 U2 U3 U4 U5 U6 U7 U8

Land cover

Wooded andshrub lands

Wooded andshrub lands

CroplandWooded andshrub lands

CroplandWooded andshrub lands

Grassland GrasslandBuilt-up

areaBuilt-up

areaBuilt-up

areaBuilt-up

areaBuilt-up

areaCropland Grassland

Built-uparea

Riparian land use

Undeveloped Residential Agricultural Undeveloped Agricultural Undeveloped Agricultural Agricultural Residential Residential Residential Residential Agricultural Agricultural Residential Residential

Stream order

1 3 3 2 2 3 3 2 2 1 3 2 2 3 3 2

Stream average width (m)

11.5 6.1 7 14 6.2 10 6.1 3.5 7.6 6.8 12.7 10.9 6.16 7.6 17.1 9.4

Stream averagedepth (m)

0.4 0.2 0.4 0.6 0.3 0.5 0.3 0.2 0.7 0.4 0.5 0.3 0.5 0.4 0.5 0.4

Supplementary Table S2. Variation in total benthic macroinvertebrates abundance across 16 sites in Marikina Watershed, the Philippines.

TaxaAbundance

P1 P2 P3 P4 P5 P6 P7 P8 U1 U2 U3 U4 U5 U6 U7 U8Ephemeroptera Baetidae Acentrella sp. 5 19 29 20 1 17 31 8 Baetiella sp. 32 12 3 42 1 28 6 47 3 Baetis sp. 3 1 1 Caenidae Caenis sp. 2 15 17 3 144 5 1 5 5 Heptageniidae Afronurus sp. 7 2 1 18 54 38 Leptophlebiidae Choroterpes sp. 2 4 65 Thraulus sp. 1 1

Paraleptophlebia sp. 3 6 23 12 36 49 4 Leptophlebia sp. 12 1 Teloganodidae Dudgeodes sp. 1 4 Teloganodes sp. 1 2 Prosophistomatidae Prosopistoma sp. 2 Tricorythidae Sparsorythus sp. 17 69 2Plecoptera Perlidae Neoperla sp. 11 49 16 3 1 1Trichoptera Calamoceratidae Anisocentropus sp. 1 Hydropsychidae Ceratopsyche sp. 30 3 92 68 20 24 3 4 5 4 Cheumatopsyche sp. 6 21 2 36 15 3 1 5 Hydropsyche sp. 3 5 12 4 9 54 Macrostemum sp. 2 2 Potamiya sp. 1 3 Oestropsyche sp. 1 4 3 1 Polycentropodidae Polycentropus sp. 1 Cyrnellus fraternus 1 3 Philopotamidae Chimarra sp. 1 6 6 4 2 Wormaldia sp. 2 Hydroptilidae Neotrichia sp. 1 1Coleoptera

Elmidae Stenelmis sp. 3 9 16 27 2 10 12 11 5 1 1 Ancyronyx sp. 1 Optioservus sp. 1 Microcylloepus sp. 1 2 23 Sritidae Elodes sp. 1 4 2 7 2 Hydrophilidae Berosus sp. 1 1 Psephenidae Eubrianax sp. 6 2 7 57 2 Psephenoides sp. 2 3 60 61 Psephenus sp. 27 Hydrophilidae Coelostoma sp. 1 Dytiscidae Dytiscus sp. 34 Neptosternus sp. 2 1 Agabus sp. 1 Dryophidae Helichus sp. 2 Gyrinidae Gyretes sp. 1 1 1 3 1Diptera Chironomidae Chironomus spp. 2 8 1 7 7 8 6 373 Polypedilum sp. 2 15 1 Thienemannimyia sp. 1 2 Eukiefferiella sp. 1 61 Cricotopus sp. 1 Microchironomus sp. 2

Parochlus sp. 13 Robackia sp. 1 Dicrotendipes sp. 1 Pentaneurini sp. 1 1 Ephydridae Ephydridae gen. sp. 1 1 Simulidae Simulum sp. 66 62 93 54 66 105 47 25 2 6 9 Tipulidae Hexatoma sp. 1 1 1 1 Pedicia sp. 1 Muscidae Muscidae gen. sp. 1 Stratiomyidae Ptecticus sp. 1 1 Nemotelus sp. 1 Lampyridae Lampyridae gen. sp. 1 Diamesinae Diamesinae gen. sp. 1 Psychodidae Clogmia sp. 1Odonata Libellulidae Libellula sp. 3 Gomphidae Gomphidae gen. sp. 1 Euphaeidae Euphaea sp. 1 1 1 Cordulegastridae Cordulegastridae gen. sp. 1 3

Hemiptera Ochteridae Ochterus sp. 1 Hebridae Merragata sp. 1 Naucoridae Naucoris sp. 1 3Phyllodocida Nereididae Namanereis sp. 1 1Rhynchobdellida Glossiphonidae Helobdella sp. 3Arynchodellida Erpobdellidae Barbronia sp. 2 6Lepidoptera Crambidae Crambidae gen. sp. 1Mesogastropoda Thiaridae Thiara sp. 1 1 1 2 1 75 Tarebia sp. 1 1 1 Melanoides sp. 1 3 1 1 1 9Unionoida Corbiculidae Corbicula sp. 5 1 2Basommatophora Lymnaeidae Lymnaea sp. 1 1Nematomorpha

Gordiidae Gordius sp. 1Neotaenioglossa Bithyniidae Bithynia sp. 4