Evaluating Ecosystem Health of the Seven Maar Lakes of San Pablo City using the Phytoplankton Index of Biotic Integrity (Phyto-IBI)

Evaluating Ecosystem Health of the Seven Maar Lakes of San Pablo City using the Phytoplankton Index of Biotic Integrity (Phyto-IBI) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating Ecosystem Health of the Seven Maar Lakes of San Pablo City using the Phytoplankton Index of Biotic Integrity (Phyto-IBI) John Vincent R. Pleto, Mayzonee Ligaray, Francis Magbanua This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4916576/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Phytoplankton Index of Biotic Integrity (Phyto-IBI) is a multi-metric index designed to simplify extensive datasets into a single dimensionless value that could assess ecosystem health. The Seven lakes provide various ecosystem services from which different stakeholders’ benefit. However, these lakes are continuously experiencing disturbance because of anthropogenic activities. This research aimed to develop a P-IBI and Organic Pollution Index (OPI) for the seven lakes. P-IBI was developed using 21 ecological phytoplankton indices. The cumulative_R 2 and correlation index were used to select the determining indices for the final P-IBI. Canonical Correlation Analysis (CCA) was conducted to test the relationship between the metrics and P-IBI and the environmental variables. The results indicated that aquaculture lakes had lower P-IBI and OPI compared to ecotourism lakes. The four aquaculture lakes were categorized as having “moderate” P-IBI and OPI levels. During the dry season, lakes Bunot and Palakpakin were classified as having “low” P-IBI. Lake Yambo, recognized as having the best environmental conditions among the lakes, was classified as “good” P-IBI. Regarding seasonal variation, the P-IBI is generally lower during the dry season for most of the lakes. CCA revealed that several parameters significantly influenced the variation of the indices during the wet and dry seasons. In addition, regression analysis showed a positive correlation between OPI and P-IBI. These findings imply that P-IBI is indeed impacted by water quality. Based on the results, P-IBI and OPI may serve as indicators of the ecological health of the seven lakes of San Pablo. The local government may establish regulations and make informed resource management decisions based on the study results to improve and protect the lake ecosystem. Bioindicator Organic Pollution Index (OPI) index of biotic integrity seasonal variation Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION The Seven Maar Lakes of San Pablo are facing ongoing disturbance and degradation due to various human activities. The aquaculture lakes (Bunot, Calibato, Sampaloc, and Palakpakin) are particularly affected by the continuous inputs of feeds and fish wastes, leading to potential eutrophic conditions. In contrast, the ecotourism lakes (Mohicap, Pandin, and Yambo) remain less degraded due to minimal aquaculture activities. Several methods, such as water quality and biodiversity assessments, can evaluate ecological health. Current approaches for assessing aquatic ecosystems largely focus on water quality indicators and aquatic organisms (Li et al., 2022 ). However, these methods often generate vast amounts of data, which can be overwhelming, especially for those without a scientific background. Understanding the significance of each parameter can also be time-consuming. Phytoplankton or microalgae are microscopic and photosynthetic organisms that form the foundation of the aquatic food web, serving as the primary food source for zooplankton and other primary consumers. However, some phytoplankton species can cause harm to ecosystems by forming harmful algal blooms (HABs) due to high nutrient levels in the water. These blooms can produce toxins that degrade water quality, harm other aquatic organisms, and pose risks to human health. The Seven Lakes of San Pablo host a diverse phytoplankton community, with the aquaculture lakes (Bunot, Calibato, Palakpakin, and Sampaloc) being particularly prone to nutrient enrichment, which can lead to algal blooms. One effective method for aquatic ecological health evaluation is the development of a multi-metric index of biological integrity (IBI), a widely used and efficient approach which was first proposed by Karr in 1981. While multi-metric indices have been criticized for reducing complex data to a single value, their primary purpose is to simplify data presentation, making it accessible to resource managers who may not be experts in the field (Gerritsen, 1995 ). The European Water Framework Directive 2000/60 (WFD) also established a framework for protecting all water bodies, including inland, transitional, coastal, and groundwater (EC, 2000 ). The Index of Biotic Integrity has proven to be a valuable tool for assessing ecosystem quality (Gammon and Simon, 2000 ). Initially, IBI was developed for rivers using fish as indicators. Then, it has been adapted for other organism groups, including aquatic plants, macrobenthos, phytoplankton, periphyton, and microorganisms (Zhu et al., 2021 ). There are limited studies of the Index of Biotic Integrity for Phytoplankton (P-IBI) in the literature, and studies still need to be conducted in Southeast Asia—several studies about the development of the Phytoplankton Index of Biotic Integrity in lakes in China. One is the study of Zhu et al. ( 2021 ) on Lake Gehu, located in the Yangtze River Delta in China. Based on the assessment, the P-IBI during the dry and wet seasons was consistent throughout, and the overall health assessment was "moderate.” The study also found that environmental factors such as total nitrogen, the permanganate index, chlorophyll a, dissolved oxygen, ammonia nitrogen, and transparency were highly correlated with P-IBI. Another recent study in Dianchi Lake, also located in the Yangtze River Basin, reported "good" P-IBI results for both wet and dry periods, with significant correlations between P-IBI and environmental factors such as total nitrogen and electrical conductivity (Cao et al., 2024 ). Additionally, Houssou et al. (2020) assessed the impact of organic pollution on plankton communities using the Plankton Index of Biotic Integrity (P-IBI) as a tool for ecological health assessment in the Oueme River Basin. Results have shown that the developed P-IBI highly correlates with organic pollution at the different sites. The Planktonic Index of Biotic Integrity was also used to measure Lake Erie's historical changes in lake ecosystem health. It reflects the Beneficial Use Impairments (BUIs) that can provide a practical, broad-scale way to monitor changes in the water quality of lakes (Kane et al., 2009 ). In addition, P-IBI can provide managers, policymakers, scientists, and the general public with information on the lake's condition in terms of phytoplankton. This study aims to develop a Phytoplankton Index of Biotic Integrity (Phyto-IBI) to evaluate the ecological condition of San Pablo's lakes. It focuses on identifying seasonal variations in Phyto-IBI across these seven lakes. Additionally, the study assessed the Organic Pollution Index (OPI) for these lakes. Finally, it aims to establish a relationship between the developed P-IBI and various environmental parameters. METHODS Study sites The Seven (7) Maar lakes of San Pablo are small freshwater lakes located on Luzon Island, approximately 102 km from Manila. Table 1 summarizes their physical characteristics. Table 1 Location and physical characteristics of the seven lakes of San Pablo. Bunot Calibato Mohicap Palakpakin Pandin Sampaloc Yambo Location 14°04'50"N 121°20'42"E 14°06'11"N 121°22'39"E 14°07'20"N 121°20'03"E 14°06'48"N 121°20'34"E 14°06'51"N 121°22'06"E 14°04'49"N 121°20'26"E 14°07'08"N 121°21'59"E Surface area (ha)* 30.5 43.0 22.8 47.9 24.0 106.0 30.5 Max depth (m)* 23 156 30.4 7.7 61.75 27.6 38 *Mendoza et al (2019). Phytoplankton Collection and Identification Samples were collected at a depth of 0.5 m using a modified water sampler. One liter sample was collected and placed in PET bottles. The collected samples were allowed to settle in the laboratory for 24 hours and then concentrated and fixed using 1% Lugol’s iodine in 50 ml tubes for counting and identification under a microscope. An Olympus CX23 microscope was used to observe the phytoplankton. Environmental Conditions Characterization A YSI multimeter was used on-site to determine the different water quality parameters: dissolved oxygen (DO), temperature, pH, total dissolved solids (TDS), conductivity, salinity, turbidity, nitrates, ammonium, ammonia, and chlorophyll concentration. A Secchi disk was utilized to determine water transparency. In addition, water samples were collected and submitted to an accredited laboratory to analyze biological oxygen demand (BOD) and total reactive phosphorus. The Organic Pollution Index (OPI) was determined using the BOD, ammonium, and phosphate parameters. The table below shows the classification for OPI (Guasmi et al., 2010 ; Al-Asadi & Al-Hejuje, 2019 ; Leclercq, 2001). This index was determined by calculating the average of the three parameters' classes. Table 2. Organic Pollution Index (OPI) classification and the corresponding levels of the parameters. Application of Phytoplankton Index of Biotic Integrity (P-IBI) The establishment of the P-IBI on the seven lakes of San Pablo was divided into training and test sets. The training sets include the samples collected on all seven lakes for 10 sampling dates (May 2022, August 2022, November 2022, April 2023, and June to November 2023). On the other hand, the test set includes data from all seven lakes from January to May 2024. Various ecological candidate indices that represent phytoplankton community characteristics were selected for developing the P-IBI. Most of these indices were derived from the studies of Spatharis & Tsirtsis ( 2010 ) and Wu et al ( 2019 ). The indices were calculated using density. The crucial ecological indices were identified using the cumulative R² and correlation index (CoI) metrics, following the methodologies of Blanco et al. ( 2007 ) and Wu et al. ( 2012 ). Additionally, chlorophyll concentration (µg/L) was incorporated into the candidate indices, as recommended by various studies (Wu et al., 2012 ; Zhu et al., 2021 ; Wu et al., 2019 ; Cao et al., 2024 ). The following formulas are used to calculate the cumulative_R 2 and correlation index (CoI): Cumulative_ R 2 = \(\:{\sum\:}_{i=1}^{n}{r}_{s,i}^{2}\) (Eq. 1) Where \(\:{r}_{s,i}\) , represents the Spearman correlation coefficient between the index and the environmental parameter i, and n is the total number of environmental parameters. Correlation index (CoI) = \(\:\frac{\left({Cumulative}_{\_}{R}^{2}S\right)\:}{{n}^{2}}\) (Eq. 2) Where S represents the number of \(\:{r}_{s,i}\) values that are statistically significant at P < 0.05. The cumulative_R 2 values range from a minimum of 1 to a maximum of n, while the CoI values range from 0 to 1, with higher values indicating a stronger correlation between the candidate index and environmental parameters. Following the method by Wu et al. ( 2012 ), the selected ecological indices were normalized using the five-level scaling system of the European Water Framework Directive (WFD) (EC, 2000 ), with scores assigned as follows: (1) – bad, (2) – low, (3) – moderate, (4) – good, and (5) – high. The candidate metrics were scored based on the 90th, 75th, 50th, and 25th percentiles of the entire training dataset. For metrics that decrease with impairment, sites were scored as 1, 2, 3, 4, or 5 corresponding to the 90th percentiles, respectively. Conversely, for metrics that increase with impairment, the site scores were reversed: 5, 4, 3, 2, 1, according to the same percentile ranges. The final P-IBI scores were then classified into five scales based on the mean of the candidate metrics (Wu et al., 2012 ). Table 3 shows the classification and range for the resulting P-IBI. Table 3. Phytoplankton Index of Biotic Integrity (P-IBI) classification Assessing the Phytoplankton index of biotic integrity (Phyto-IBI) The Phyto-IBI was evaluated using a dataset collected from January to May 2024. The significance of the correlation between Phyto-IBI and other metrics and environmental variables was assessed using cumulative_R 2 and CoI. The P-IBI was deemed acceptable if the results from the training and testing datasets were consistent. Additionally, to assess the Phyto-IBI 's performance, the Quality Group Species (QGS) was calculated, based on the species richness of specific taxonomic groups linked to water quality (Katsiapi et al., 2016 ; Watson et al., 1997 ). Data Analysis Canonical correlation analysis (CCA) was used to test the relationship among the metrics and Phyto-IBI with the environmental variables. Wu et al ( 2012 ) used this multivariate ordination technique to evaluate species-environmental variable relationship. Linear regression analysis was used to assess the relationship between the P-IBI and OPI values. Wu et al ( 2019 ) used linear regression to test the relationship of P-IBI to their resulting water quality index (WQI) Graphs and statistical analyses were generated using R software. RESULTS Metrics Selection and Identification for the Development of Phyto-IBI for Seven Lakes Spearman’s correlation analysis revealed no correlations among the 18 environmental parameters studied. These environmental parameters are included in the calculations of Cumulative_R 2 and CoI. Table 4 shows the selected indices' resulting Cumulative_R 2 and correlation index (CoI). The criteria for the selected indices—high Cumulative_R 2 and CoI values greater than 0.03 for both training and testing datasets—were included in determining the P-IBI. With these criteria, chlorophyll a, density, Menhinick, and Evenness E 2 (Pielou) were the selected candidates to calculate the P-IBI of the seven lakes. Though the Menhinick index had less than 0.03 CoI on the training dataset, it recorded a very high CoI in the testing dataset to justify its inclusion in the final P-IBI. Three of the four chosen indices are similar to the studies of Wu et al. ( 2012 ) and Wu et al. ( 2019 ). In their studies, these authors found that chlorophyll a and density responded positively to water quality deterioration, while Menhinick and Evenness E2 responded negatively. Table 4 Cumulative_R 2 and correlation index (CoI) values of the selected indices for the P-IBI of the training and testing data sets. Metrics (Response to Deterioration) Cumulative_ R 2 Correlation Index (CoI) Training Testing Training Testing Chl-a (+) 9.719 19.645 0.595 0.656 Density (+) 1.983 18.018 0.091 0.113 Menhinick (-) 0.018 9.51 0.00 0.408 Evenness_E 2 (-) 1.127 3.499 0.04 0.062 QGS 10.511 0.050 0.536 0.0002 P-IBI 1.510 19.922 0.046 1.321 Relationship between environmental variables and the proposed Phyto-IBI Using the training dataset, the proposed P-IBI was found to be significantly correlated to 10 environmental variables (dissolved oxygen, total dissolved solids, conductivity, salinity, turbidity, total suspended solids, Secchi disk transparency, biological oxygen demand, ammonium, and phosphate) (Table 2 ). The Cumulative_R 2 and CoI of the four selected indices increased on the testing dataset, with the Phyto-IBI providing a higher correlation than the QGS in the testing dataset (Table 1 ). Table 5 Spearman rank correlation coefficients of the chosen indices and environmental variables using the training dataset. Environmental Parameters Indices Chl a Density Menhinick Evenness (E2) P-IBI Dissolved Oxygen 0.142* 0.057 0.088 0.022 0.225*** Temperature 0.051 0.011 -0.085 -0.015 -0.032* pH 0.124* 0.054 -0.025 0.057 0.030 Total Dissolved Solids 0.375*** 0.124* -0.050 -0.214*** 0.084*** Conductivity 0.380*** 0.150* -0.049 -0.206*** 0.105*** Salinity 0.400*** 0.130* -0.031 -0.205*** 0.104*** Turbidity 0.468*** 0.281*** 0.128* -0.135* 0.406*** Total Suspended Solids 0.509*** 0.209*** 0.058 -0.060 0.289*** Secchi Disk Transparency -0.639*** -0.329*** -0.102 0.136* -0.385*** Biological Oxygen Demand 0.533*** 0.216*** 0.050 -0.098 0.280*** Ammonium 0.280*** 0.178** -0.141* -0.192*** 0.119 Ammonia 0.003 0.034 -0.035 -0.021 -0.122 Nitrate 0.11 0.054 0.144* 0.041 0.052 Phosphate 0.376*** 0.240*** -0.067 -0.171*** 0.074*** *P < 0.05, **P < 0.01, *** P < 0.001 Organic Pollution Index (OPI) of the 7 Lakes of San Pablo Figure 1 shows the organic pollution index of the seven lakes during the wet and dry seasons. During the wet and dry seasons, Lakes Pandin and Yambo were categorized as having “less organic pollution.” Minimal variation was observed in these two lakes when the wet and dry seasons were compared. Lake Mohicap was considered to have moderate organic pollution during the wet season and less organic pollution during the dry season. Lakes Bunot, Calibato, Palakpakin, and Sampaloc were classified as having “moderate organic pollution” during the wet and dry seasons. The aquaculture-intensive lakes fall within the moderate organic pollution level, while the ecotourism lakes were categorized as having less organic pollution for both seasons. The organic pollution in the four lakes is due to relatively high levels of phosphorus and BOD in the surface water. Phytoplankton Index of Biotic Integrity (Phyto-IBI) of the Seven Lakes of San Pablo The resulting P-IBI for the seven lakes is shown in Fig. 2 for both wet and dry season sampling. Lake Bunot had a low Phyto-IBI during the dry season and moderate Phyto-IBI during the wet season. Both wet and dry seasons show similar median values for Lake Calibato, which also translates to moderate status. Lake Mohicap had a slightly higher Phyto-IBI during the dry season compared to the wet season. A moderate Phyto-IBI was reflected in both seasons. During the dry season, Lake Palakpakin reflected a low P-IBI status, increasing to moderate in the wet season. Lake Pandin had moderate Phyto-IBI status during the dry season and good status during the wet season. In both seasons, a moderate status was reflected on Lake Sampaloc. Lastly, Lake Yambo reflected a good condition for both wet and dry. Most lakes maintained a moderate Phyto-IBI status except for Lakes Bunot and Palakpakin, which fell to a low Phyto-IBI status during the dry season. Out of the seven lakes, only Lake Yambo reflected a good Phyto-IBI condition for both the wet and dry seasons, which indicates a relatively healthy phytoplankton community. Lakes Bunot and Palakpakin show lower Phyto-IBI during the dry season, which could indicate poorer water quality or less healthy phytoplankton communities in this period. Relationship of Phyto-IBI with environmental parameters Canonical Correlation Analysis (CCA) is a multivariate ordination method used to understand the relationships between the species, environmental variables, P-IBI and OPI results. This would help reveal and provide insights into how environmental factors, P-IBI and OPI influence species. The CCA result of the wet season sampling indicated that CCA1 explains 24.14% of the variance in species metrics, while CCA2 explains 0.89%. BOD, conductivity, salinity, TDS, phosphates, TSS, and turbidity positively correlate with the CCA1 and CCA2 axes. Turbidity and phosphate have strong positive scores, indicating that higher levels are associated with the species data. ANOVA-CCA revealed that the model has a statistically significant (p < 0.001) relationship. DO, turbidity, TSS, and ammonia are predictors that could explain the variation of the species metrics. On the other hand, the CCA result of the dry season suggested that 19.52% of the total variation of species metrics was attributed to environmental parameters. Eight parameters (TDS, conductivity, salinity, turbidity, BOD, ammonia, ammonium, and phosphate) positively correlate with CCA1 and CCA2. Conductivity and phosphate have a strong positive correlation with the species metrics data. No statistically significant relationship exists between species metrics and environmental variables during the dry season. Both SDT and OPI are significant predictors that explain the variation of the species metrics data. The density in wet and dry seasons is close to the origin, indicating that multiple environmental variables influence this metric. Phyto-IBI during the wet and dry seasons is likely associated with nitrates. The linear regression analysis of the Phyto-IBI and OPI was significantly and positively correlated for both wet and dry seasons. In the wet season, the R 2 value is 0.29 with p < 0.001, while in the dry season, the R 2 value is 0.33 with p < 0.001 (Fig. 4 ). DISCUSSION Organic pollution in the Seven Lakes Nutrients could significantly affect the growth of phytoplankton in lakes. When nitrogen and phosphorus increase due to natural and anthropogenic activities, lake water could lead to eutrophication, which causes excessive algal growth that could lead to algal blooms. Harmful algal blooms (HABs) could produce toxins harmful to aquatic organisms, and primarily, cyanobacteria are responsible for bloom occurrence. High nutrient loads can promote the production of organic matter (Huang et al., 2022 ). The seven lakes of San Pablo are highly exposed to anthropogenic activities, which include domestic, livelihood (aquaculture), and eco-tourism. According to the study of Dimzon et al. ( 2018 ), the large population and massive load of untreated domestic waste contributed to the organic pollution in Lake Sampaloc. For Lake Palakpakin, the high volume of aquaculture pens contributes to the organic pollution in the lake. Different land uses surrounding the lakes, such as plantations and rice fields, also contribute to the pollution load in the lake. Based on the organic pollution index assessment, the ecotourism lakes Pandin and Yambo were categorized as having “less organic pollution.” This is due to the fewer aquaculture activities that can contribute to the pollution load. It was also highlighted that aquaculture lakes (Bunot, Calibato, Sampaloc, and Palakpakin) had “moderate organic pollution”. Anthropogenic aquaculture activities can cause high water BOD values (Sallam and Elsayed, 2018 ). Gondwe et al. ( 2011 ) found that 81–90% of organic waste released during tilapia cage culture is discharged into the water body. The fish cages are also characterized by releasing nitrogen, phosphorus, and organic matter that causes nutrient enrichment, which fastens the production rate and leads to eutrophication (Lubembe et al., 2024 ). Overall, the results showed that aquaculture activities contributed to the lakes' organic pollution. Phytoplankton Index of Biotic Integrity (Phyto-IBI) of the Seven Lakes A key step in developing the P-IBI is selecting appropriate ecological indices. Wu et al. ( 2012 ) established their P-IBI is using six metrics: chlorophyll-a, Saprobity index, Cyanobacteria index, Margalef’s index, species richness, and Menhinick index. Spatharis and Tsirtsis ( 2010 ) simulated phytoplankton communities to standardize and minimize the error in the data collected and choosing the indices. In addition, the authors suggested that chlorophyll a, density, and Menhinick index were suitable for ecological quality assessment. The mentioned indices exhibited a relatively strong relationship with environmental factors. This study adopted chlorophyll a, density, Menhinick index, and Evenness E 2 as key indices. Phytoplankton community structure is influenced by various environmental parameters (Huszar et al., 2015 ). High nutrients would favor phytoplankton growth, and blooms are always associated with a high load of nutrients (Xu et al., 2010 ). The resulting Phyto-IBI reflected that two (Bunot and Palakpakin) of the seven lakes recorded a “low” assessment during the dry season. The selected indices reflected the condition of the seven lakes, wherein the two lakes (Bunot and Palakpakin) are loaded with high levels of nutrients and organic matter due to aquaculture activities. Generally, the most effective indices that reflect alterations in the phytoplankton community structure are productivity and species density (Devlin et al., 2007 ). In the study of Zhu et al. ( 2021 ) in Lake Gehu, it was found that Phyto-IBI in the dry season was higher compared to the wet season. These authors claimed that increased temperature during the summer leads to increased cyanobacteria and decreased water quality. However, this study showed that most lakes have lower Phyto-IBI values during the dry season. This might be due to the high phytoplankton density and chlorophyll level that affected the Phyto-IBI value. The diversity during the dry season is relatively low due to the dominance of some phytoplankton. Relationship of Phyto-IBI with environmental variables Several studies have found that Phyto-IBI showed high sensitivity to water quality deterioration. A study by Zhu et al. ( 2021 ) indicated that Phyto-IBI and its constituent parameters were highly correlated with water quality factors. Their study has found that the main water quality parameters affecting the Phyto-IBI were total nitrogen, permanganate index, chlorophyll, and dissolved oxygen. Nutrient concentrations significantly affect the phytoplankton community because an elevated concentration could lead to eutrophication. This could cause water quality degradation due to harmful algal blooms. High nutrients correspond to lower Phyto-IBI scores, which indicates poor ecological health (Smith et al. 1999 ). In San Pablo, aquaculture lakes (Bunot, Calibato, Palakpakin, and Sampaloc) tend to have higher nutrient concentrations, leading to lower Phyto-IBI values. Another water parameter that could impact the Phyto-IBI result is temperature. Reynolds ( 2006 ) indicated that temperature affects the metabolic rate of phytoplankton and seasonal distribution. Warmer temperature increases growth rates that could favor the dominance of less desirable or toxic species. This could lower the Phyto-IBI value. The study of Cao et al. ( 2024 ) found that electrical conductivity (EC) had a significant positive correlation with Phyto-IBI values for both wet and dry seasons in Lake Dianchi. The study by Wu et al. ( 2019 ) in the Lake Taihu Basin (LTB) exhibited a positive correlation between Phyto-IBI and the Water Quality Index (WQI) on all sites. This would justify that water quality influenced the Phyto-IBI since it impacts the growth, composition, and distribution of phytoplankton communities in aquatic ecosystems. Importance of Developing Multi-metric Index for management The multi-metric approach or index of biotic integrity (IBI) was originally developed by Karr ( 1981 ). It was the most common indicator used to characterize stream conditions. This has also been criticized because multi-metric indices reduced the data into a single dimensionless number. However, Gerritsen ( 1995 ) argued that the goal of IBI is for data simplification, and this feature is advantageous for resource managers who are not experts in the field. This could also provide an easy understanding of the current status rather than presenting large amounts of data to be interpreted. This study supported the claim of Gerritsen ( 1995 ) and other IBI studies that using a multi-metric index in biological data could provide a reliable assessment of ecosystem quality. Most researchers believe that developing an index of biological integrity has certain limitations in evaluating ecological health. However, it is still an effective evaluation method (Zhu et al., 2021 ). Ruaro and Gubiani ( 2013 ) suggest that simple indices are ideal for the rapid and efficient monitoring of freshwater environments, particularly in developing countries such as the Philippines, where research on P-IBI is still lacking. The Phyto-IBI would provide a more comprehensive approach to evaluating ecosystem health. With the observed results of low Phyto-IBI for two lakes (Bunot and Palakpakin) during the dry season, efforts must be made by the local government unit of San Pablo to improve its condition by reducing the nutrient load in the water. This could prevent phytoplankton blooms from deteriorating the lake ecosystem. Lakes Yambo and Pandin are the ideal lakes for the P-IBI values. It reflected good ecological health. The LGU can use the result of P-IBI to identify impaired lakes that need remediation. It can also provide scientific data for policy development since it can offer an objective basis for establishing regulations and making resource management decisions to improve and protect environmental quality. CONCLUSION The development of Phytoplankton Index of Biotic Integrity (Phyto-IBI) for the seven lakes of San Pablo is an effective tool for assessing the condition of the lake ecosystem. It can be a comprehensive ecosystem health indicator integrating biological and environmental factors. The identified metrics for developing the Phyto-IBI include the chlorophyll a, density, Menhinick index, and Evenness E2 (Pielou) index. Based on the calculated Phyto-IBI, it showed that the aquaculture lakes (Bunot, Calibato, Sampaloc, and Palakpakin) had lower Phyto-IBI compared to the ecotourism lakes (Mohicap, Pandin, and Yambo). It also reflected that a lower Phyto-IBI was observed for most of the lakes during the dry season. The aquaculture lakes were classified as “moderate” during the wet and dry seasons. However, two lakes (Bunot and Palakpakin) were classified as “low” Phyto-IBI during the dry season. Lake Yambo was classified as “good” for both seasons, which could be the ideal or model lake for all seven lakes. The OPI indicated that the four aquaculture lakes have “moderate organic pollution” for both seasons, while the three other ecotourism lakes had “less organic pollution.” Among the environmental parameters, CCA revealed that TSS, turbidity, phosphorus, TDS, conductivity, salinity, and BOD significantly influenced the variation of the chosen indices. Linear regression analysis showed that Phyto-IBI and OPI were significantly and positively correlated for both seasons. With these perceived outcomes, the LGU can use it to establish regulations and make resource management decisions to improve and protect the ecosystem health of the seven lakes of San Pablo. Declarations Acknowledgments The research team expresses its deep appreciation to the Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development (PCAARRD), Department of Science and Technology (DOST) for their invaluable support under the project “Development of Models for the Assessment and Monitoring of the Seven Lakes of San Pablo”. We are also sincerely grateful to the LGU of San Pablo City, FARM-C, and the fisherfolk of each lake for their vital assistance during the study. Additionally, we extend our heartfelt thanks to Mr. Lawrence Victor Vitug, the Project Technical Assistant, for his expertise in identifying the phytoplankton species. Funding Declaration The research was conducted under the project Development of Models for the Assessment and Monitoring of the Seven Lakes of San Pablo (Project No. N92732A), which was and funded by the Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development (PCAARRD), Department of Science and Technology (DOST). Ethical Declaration This study was conducted without involving human participants, animals, or sensitive personal data. Therefore, the standard ethical considerations related to the protection of human and animal subjects, informed consent, privacy, and confidentiality are not applicable. Authorship Contribution John Vincent R. Pleto : Conceptualization of the research, Methodology (Data Collection), Data Visualization and Analysis, Investigation, Writing – original draft, Formal Analysis. Mayzonee D. Ligaray : Conceptualization of the research, Writing – review and editing of the manuscript. Francis S. 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Li Z, Ma C, Sun Y, Lu X and Fan Y (2022) Ecological health evaluation of rivers based on phytoplankton biological integrity index and water quality index on the impact of anthropogenic pollution: A case of Ashi River Basin. Front. Microbiol . 13:942205. doi: 10.3389/fmicb.2022.942205 Lubembe, S. I., Walumona, J. R., Hyangya, B. L., Kondowe, B. N., Kulimushi, J.-D. M., Shamamba, G. A., Kulimushi, A. M., Hounsounou, B. H. R., Mbalassa, M., Masese, F. O., & Masilya, M. P. (2024). Environmental impacts of tilapia fish cage aquaculture on water physico-chemical parameters of Lake Kivu, Democratic Republic of the Congo. Frontiers in Water , 6, 1325967. https://doi.org/10.3389/frwa.2024.1325967 Kane, D. D., Gordon, S. I., Munawar, M., Charlton, M. N., & Culver, D. A. (2009). The Planktonic Index of Biotic Integrity (P-IBI): An approach for assessing lake ecosystem health. Ecological Indicators , 9(6), 1234–1247. https://doi.org/10.1016/j.ecolind.2009.03.014. Katsiapi, M., Moustaka-Gouni, M., & Sommer, U. (2016). Assessing ecological water quality of freshwaters: PhyCoI—a new phytoplankton community Index. Ecological Informatics , 31, 22–29. https://doi.org/10.1016/j.ecoinf.2015.11.004 Karr, J.R., 1981. Assessment of biotic integrity using fish communities. Fisheries 6, 21–27. Reynolds, C. S. (2006). The Ecology of Phytoplankton . Cambridge University Press. Ruaro, R., Gubiani, E.A., 2013. A scientometric assessment of 30 years of the Index of Biotic Integrity in aquatic ecosystems: applications andmain flaws. Ecological Indicators . 29, 105–110. Sallam, G.A. and Elsayed, E.A. 2018. Estimating relations between temperature, relative humidity, as independent variables and selected water quality parameters in Lake Manzala, Egypt. Ain Shams Eng J . 9(1), 1-14. Smith, V. H., Tilman, G. D., & Nekola, J. C. (1999). Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environmental Pollution , 100(1-3), 179-196. Spatharis, S., & Tsirtsis, G. (2010). Ecological quality scales based on phytoplankton for the implementation of Water Framework Directive in the Eastern Mediterranean. Ecological Indicators , 10(4), 840–847. https://doi.org/10.1016/j.ecolind.2010.01.005 Watson, S.B., McCauley, E., Dawning, J.A., 1997. Patterns in phytoplankton taxonomic composition across temperate lakes of different nutrient status. Limnol. Oceanogr . 42, 487–495. Wu, N., Schmalz, B., & Fohrer, N. (2012). Development and testing of a phytoplankton index of biotic integrity (P-IBI) for a German lowland river. Ecological Indicators , 13(1), 158–167. https://doi.org/10.1016/j.ecolind.2011.05.022. Wu, Z.S., Wang, X.L., Chen, Y.W., Cai, Y.J., Deng, J.C., 2018. Assessing river water quality using water quality index in Lake Taihu Basin, China. Science of The Total Environment . 612, 914–922. Wu, Z., Kong, M., Cai, Y., Wang, X., & Li, K. (2019). Index of biotic integrity based on phytoplankton and water quality index: Do they have a similar pattern on water quality assessment? A study of rivers in Lake Taihu Basin, China. Science of The Total Environment , 658, 395–404. https://doi.org/10.1016/j.scitotenv.2018.12.216 Xu, H., Paerl, H.W., Qin, B.Q., Zhu, G.W., Gao, G., 2010. Nitrogen and phosphorus inputs control phytoplankton growth in eutrophic Lake Taihu, China. Limnol. Oceanogr . 55, 420–432. Zhu, H., Hu, X.-D., Wu, P.-P., Chen, W.-M., Wu, S.-S., Li, Z.-Q., Zhu, L., Xi, Y.-L., & Huang, R. (2021). Development and testing of the phytoplankton biological integrity index (P-IBI) in dry and wet seasons for Lake Gehu. Ecological Indicators , 129, 107882. https://doi.org/10.1016/j.ecolind.2021.107882. Additional Declarations No competing interests reported. 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Pleto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACxgYGxgNAWg7M4wFiAyK0MBxgSGAwJl4LCIC0JDYQrYV59tkDBz7+sEnfcLuB8cHbNoY8c4IO68tLODgjIS13w50DzIZz2xiKLRsIaenhMTjMk3A4d8ONBDZp3jaGxA0HiNHyJ+FwusGNBPbfxGthSDicANTCxkykFr6Egz1paYYz7xxslpxzTqLYgJAWwx7egw9+2NjI891uPvjhTZlNHmEtDTxQlgQoVhkkEghoYGCQZ4BrgVCEtYyCUTAKRsGIAwAJjUX2IxJcKgAAAABJRU5ErkJggg==","orcid":"","institution":"University of the Philippines Diliman","correspondingAuthor":true,"prefix":"","firstName":"John","middleName":"Vincent R.","lastName":"Pleto","suffix":""},{"id":357624395,"identity":"d7e33115-ce99-4b33-bf84-44090e1e8acd","order_by":1,"name":"Mayzonee Ligaray","email":"","orcid":"","institution":"University of the Philippines Diliman","correspondingAuthor":false,"prefix":"","firstName":"Mayzonee","middleName":"","lastName":"Ligaray","suffix":""},{"id":357624396,"identity":"5111b620-943d-4efa-92a9-ffe4ce265426","order_by":2,"name":"Francis Magbanua","email":"","orcid":"","institution":"University of the Philippines Diliman","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"","lastName":"Magbanua","suffix":""}],"badges":[],"createdAt":"2024-08-15 02:23:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4916576/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4916576/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65345706,"identity":"c68dae50-0693-4efb-bc0a-4a01ba9deb17","added_by":"auto","created_at":"2024-09-26 09:39:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":175958,"visible":true,"origin":"","legend":"\u003cp\u003eOrganic Pollution Index (OPI) of the seven lakes during the wet season (June–November 2023) and dry season (January–May 2024).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4916576/v1/06dc1b3ff0768aeec69185b9.png"},{"id":65345707,"identity":"9e24a0cc-5219-44dd-9dd5-837d05650311","added_by":"auto","created_at":"2024-09-26 09:39:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":224421,"visible":true,"origin":"","legend":"\u003cp\u003ePhytoplankton Index of Biotic Integrity (Phyto-IBI) of the seven lakes during the wet season (June–November 2023) and dry season (January–May 2024).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4916576/v1/49b8c1f99bf789b7749efb6d.png"},{"id":65345708,"identity":"e1d6dba9-ef35-4105-a61d-8f7b95ca8836","added_by":"auto","created_at":"2024-09-26 09:39:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":933390,"visible":true,"origin":"","legend":"\u003cp\u003eCanonical Correspondence Analysis (CCA) of the wet and dry season sampling.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4916576/v1/94a4cf0d1546e0be39673765.png"},{"id":65345709,"identity":"feecb343-f551-485a-84fd-93217273b24a","added_by":"auto","created_at":"2024-09-26 09:39:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":180744,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression analysis of the Phyto-IBI and OPI for wet and dry seasons.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4916576/v1/37a41d8af0762935055d7e78.png"},{"id":66322749,"identity":"c729be11-b536-4762-8c9d-8e04cc87d4c7","added_by":"auto","created_at":"2024-10-10 12:16:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2205854,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4916576/v1/5a28f9b8-3dc8-401b-8300-a22eb4fd19a4.pdf"},{"id":65345705,"identity":"55ad3e70-4b97-4610-b441-d8e691d9b116","added_by":"auto","created_at":"2024-09-26 09:39:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17824,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYTABLES.docx","url":"https://assets-eu.researchsquare.com/files/rs-4916576/v1/106c018698fb565c32a1b02c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Ecosystem Health of the Seven Maar Lakes of San Pablo City using the Phytoplankton Index of Biotic Integrity (Phyto-IBI)","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe Seven Maar Lakes of San Pablo are facing ongoing disturbance and degradation due to various human activities. The aquaculture lakes (Bunot, Calibato, Sampaloc, and Palakpakin) are particularly affected by the continuous inputs of feeds and fish wastes, leading to potential eutrophic conditions. In contrast, the ecotourism lakes (Mohicap, Pandin, and Yambo) remain less degraded due to minimal aquaculture activities.\u003c/p\u003e \u003cp\u003eSeveral methods, such as water quality and biodiversity assessments, can evaluate ecological health. Current approaches for assessing aquatic ecosystems largely focus on water quality indicators and aquatic organisms (Li et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, these methods often generate vast amounts of data, which can be overwhelming, especially for those without a scientific background. Understanding the significance of each parameter can also be time-consuming.\u003c/p\u003e \u003cp\u003ePhytoplankton or microalgae are microscopic and photosynthetic organisms that form the foundation of the aquatic food web, serving as the primary food source for zooplankton and other primary consumers. However, some phytoplankton species can cause harm to ecosystems by forming harmful algal blooms (HABs) due to high nutrient levels in the water. These blooms can produce toxins that degrade water quality, harm other aquatic organisms, and pose risks to human health. The Seven Lakes of San Pablo host a diverse phytoplankton community, with the aquaculture lakes (Bunot, Calibato, Palakpakin, and Sampaloc) being particularly prone to nutrient enrichment, which can lead to algal blooms.\u003c/p\u003e \u003cp\u003eOne effective method for aquatic ecological health evaluation is the development of a multi-metric index of biological integrity (IBI), a widely used and efficient approach which was first proposed by Karr in 1981. While multi-metric indices have been criticized for reducing complex data to a single value, their primary purpose is to simplify data presentation, making it accessible to resource managers who may not be experts in the field (Gerritsen, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). The European Water Framework Directive 2000/60 (WFD) also established a framework for protecting all water bodies, including inland, transitional, coastal, and groundwater (EC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Index of Biotic Integrity has proven to be a valuable tool for assessing ecosystem quality (Gammon and Simon, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Initially, IBI was developed for rivers using fish as indicators. Then, it has been adapted for other organism groups, including aquatic plants, macrobenthos, phytoplankton, periphyton, and microorganisms (Zhu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There are limited studies of the Index of Biotic Integrity for Phytoplankton (P-IBI) in the literature, and studies still need to be conducted in Southeast Asia\u0026mdash;several studies about the development of the Phytoplankton Index of Biotic Integrity in lakes in China. One is the study of Zhu et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) on Lake Gehu, located in the Yangtze River Delta in China. Based on the assessment, the P-IBI during the dry and wet seasons was consistent throughout, and the overall health assessment was \"moderate.\u0026rdquo; The study also found that environmental factors such as total nitrogen, the permanganate index, chlorophyll a, dissolved oxygen, ammonia nitrogen, and transparency were highly correlated with P-IBI. Another recent study in Dianchi Lake, also located in the Yangtze River Basin, reported \"good\" P-IBI results for both wet and dry periods, with significant correlations between P-IBI and environmental factors such as total nitrogen and electrical conductivity (Cao et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, Houssou et al. (2020) assessed the impact of organic pollution on plankton communities using the Plankton Index of Biotic Integrity (P-IBI) as a tool for ecological health assessment in the Oueme River Basin. Results have shown that the developed P-IBI highly correlates with organic pollution at the different sites. The Planktonic Index of Biotic Integrity was also used to measure Lake Erie's historical changes in lake ecosystem health. It reflects the Beneficial Use Impairments (BUIs) that can provide a practical, broad-scale way to monitor changes in the water quality of lakes (Kane et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In addition, P-IBI can provide managers, policymakers, scientists, and the general public with information on the lake's condition in terms of phytoplankton.\u003c/p\u003e \u003cp\u003eThis study aims to develop a Phytoplankton Index of Biotic Integrity (Phyto-IBI) to evaluate the ecological condition of San Pablo's lakes. It focuses on identifying seasonal variations in Phyto-IBI across these seven lakes. Additionally, the study assessed the Organic Pollution Index (OPI) for these lakes. Finally, it aims to establish a relationship between the developed P-IBI and various environmental parameters.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy sites\u003c/h2\u003e\n \u003cp\u003eThe Seven (7) Maar lakes of San Pablo are small freshwater lakes located on Luzon Island, approximately 102 km from Manila. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes their physical characteristics.\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLocation and physical characteristics of the seven lakes of San Pablo.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBunot\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCalibato\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMohicap\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePalakpakin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePandin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSampaloc\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYambo\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026deg;04\u0026apos;50\u0026quot;N 121\u0026deg;20\u0026apos;42\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026deg;06\u0026apos;11\u0026quot;N 121\u0026deg;22\u0026apos;39\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026deg;07\u0026apos;20\u0026quot;N 121\u0026deg;20\u0026apos;03\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026deg;06\u0026apos;48\u0026quot;N 121\u0026deg;20\u0026apos;34\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026deg;06\u0026apos;51\u0026quot;N 121\u0026deg;22\u0026apos;06\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026deg;04\u0026apos;49\u0026quot;N 121\u0026deg;20\u0026apos;26\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026deg;07\u0026apos;08\u0026quot;N 121\u0026deg;21\u0026apos;59\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurface area (ha)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax depth (m)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e*Mendoza et al (2019).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003ePhytoplankton Collection and Identification\u003c/h2\u003e\n \u003cp\u003eSamples were collected at a depth of 0.5 m using a modified water sampler. One liter sample was collected and placed in PET bottles. The collected samples were allowed to settle in the laboratory for 24 hours and then concentrated and fixed using 1% Lugol\u0026rsquo;s iodine in 50 ml tubes for counting and identification under a microscope. An Olympus CX23 microscope was used to observe the phytoplankton.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eEnvironmental Conditions Characterization\u003c/h2\u003e\n \u003cp\u003eA YSI multimeter was used on-site to determine the different water quality parameters: dissolved oxygen (DO), temperature, pH, total dissolved solids (TDS), conductivity, salinity, turbidity, nitrates, ammonium, ammonia, and chlorophyll concentration. A Secchi disk was utilized to determine water transparency. In addition, water samples were collected and submitted to an accredited laboratory to analyze biological oxygen demand (BOD) and total reactive phosphorus.\u003c/p\u003e\n \u003cp\u003eThe Organic Pollution Index (OPI) was determined using the BOD, ammonium, and phosphate parameters. The table below shows the classification for OPI (Guasmi et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Al-Asadi \u0026amp; Al-Hejuje, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Leclercq, 2001). This index was determined by calculating the average of the three parameters\u0026apos; classes.\u003c/p\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003eTable 2. Organic Pollution Index (OPI) classification and the corresponding levels of the parameters.\u003cbr\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img172734339414.png\"\u003e\u003c/div\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003eApplication of Phytoplankton Index of Biotic Integrity (P-IBI)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003cp\u003eThe establishment of the P-IBI on the seven lakes of San Pablo was divided into training and test sets. The training sets include the samples collected on all seven lakes for 10 sampling dates (May 2022, August 2022, November 2022, April 2023, and June to November 2023). On the other hand, the test set includes data from all seven lakes from January to May 2024.\u003c/p\u003e\n \u003cp\u003eVarious ecological candidate indices that represent phytoplankton community characteristics were selected for developing the P-IBI. Most of these indices were derived from the studies of Spatharis \u0026amp; Tsirtsis (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Wu et al (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The indices were calculated using density. The crucial ecological indices were identified using the cumulative R\u0026sup2; and correlation index (CoI) metrics, following the methodologies of Blanco et al. (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Wu et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, chlorophyll concentration (\u0026micro;g/L) was incorporated into the candidate indices, as recommended by various studies (Wu et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cao et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The following formulas are used to calculate the cumulative_R\u003csup\u003e2\u003c/sup\u003e and correlation index (CoI):\u003c/p\u003e\n \u003cp\u003eCumulative_ R\u003csup\u003e2\u003c/sup\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=1}^{n}{r}_{s,i}^{2}\\)\u003c/span\u003e\u003c/span\u003e (Eq.\u0026nbsp;1)\u003c/p\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{s,i}\\)\u003c/span\u003e\u003c/span\u003e, represents the Spearman correlation coefficient between the index and the environmental parameter i, and n is the total number of environmental parameters.\u003c/p\u003e\n \u003cp\u003eCorrelation index (CoI) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\left({Cumulative}_{\\_}{R}^{2}S\\right)\\:}{{n}^{2}}\\)\u003c/span\u003e\u003c/span\u003e (Eq. 2)\u003c/p\u003e\n \u003cp\u003eWhere S represents the number of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{s,i}\\)\u003c/span\u003e\u003c/span\u003e values that are statistically significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The cumulative_R\u003csup\u003e2\u003c/sup\u003e values range from a minimum of 1 to a maximum of n, while the CoI values range from 0 to 1, with higher values indicating a stronger correlation between the candidate index and environmental parameters. Following the method by Wu et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), the selected ecological indices were normalized using the five-level scaling system of the European Water Framework Directive (WFD) (EC, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e), with scores assigned as follows: (1) \u0026ndash; bad, (2) \u0026ndash; low, (3) \u0026ndash; moderate, (4) \u0026ndash; good, and (5) \u0026ndash; high. The candidate metrics were scored based on the 90th, 75th, 50th, and 25th percentiles of the entire training dataset. For metrics that decrease with impairment, sites were scored as 1, 2, 3, 4, or 5 corresponding to the \u0026lt;\u0026thinsp;25th, 25th\u0026ndash;50th, 50th\u0026ndash;75th, 75th\u0026ndash;90th, and \u0026gt;\u0026thinsp;90th percentiles, respectively. Conversely, for metrics that increase with impairment, the site scores were reversed: 5, 4, 3, 2, 1, according to the same percentile ranges. The final P-IBI scores were then classified into five scales based on the mean of the candidate metrics (Wu et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the classification and range for the resulting P-IBI.\u003c/p\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003eTable 3. Phytoplankton Index of Biotic Integrity (P-IBI) classification\u003c/div\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1727343394.png\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eAssessing the Phytoplankton index of biotic integrity (Phyto-IBI)\u003c/h2\u003e\n \u003cp\u003eThe Phyto-IBI was evaluated using a dataset collected from January to May 2024. The significance of the correlation between Phyto-IBI and other metrics and environmental variables was assessed using cumulative_R\u003csup\u003e2\u003c/sup\u003e and CoI. The P-IBI was deemed acceptable if the results from the training and testing datasets were consistent. Additionally, to assess the Phyto-IBI \u0026apos;s performance, the Quality Group Species (QGS) was calculated, based on the species richness of specific taxonomic groups linked to water quality (Katsiapi et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Watson et al., \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eData Analysis\u003c/h2\u003e\n \u003cp\u003eCanonical correlation analysis (CCA) was used to test the relationship among the metrics and Phyto-IBI with the environmental variables. Wu et al (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) used this multivariate ordination technique to evaluate species-environmental variable relationship. Linear regression analysis was used to assess the relationship between the P-IBI and OPI values. Wu et al (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) used linear regression to test the relationship of P-IBI to their resulting water quality index (WQI) Graphs and statistical analyses were generated using R software.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMetrics Selection and Identification for the Development of Phyto-IBI for Seven Lakes\u003c/h2\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation analysis revealed no correlations among the 18 environmental parameters studied. These environmental parameters are included in the calculations of Cumulative_R\u003csup\u003e2\u003c/sup\u003e and CoI. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the selected indices' resulting Cumulative_R\u003csup\u003e2\u003c/sup\u003e and correlation index (CoI). The criteria for the selected indices\u0026mdash;high Cumulative_R\u003csup\u003e2\u003c/sup\u003e and CoI values greater than 0.03 for both training and testing datasets\u0026mdash;were included in determining the P-IBI. With these criteria, chlorophyll a, density, Menhinick, and Evenness E\u003csub\u003e2\u003c/sub\u003e (Pielou) were the selected candidates to calculate the P-IBI of the seven lakes. Though the Menhinick index had less than 0.03 CoI on the training dataset, it recorded a very high CoI in the testing dataset to justify its inclusion in the final P-IBI. Three of the four chosen indices are similar to the studies of Wu et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Wu et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In their studies, these authors found that chlorophyll a and density responded positively to water quality deterioration, while Menhinick and Evenness E2 responded negatively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCumulative_R\u003csup\u003e2\u003c/sup\u003e and correlation index (CoI) values of the selected indices for the P-IBI of the training and testing data sets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetrics (Response to Deterioration)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCumulative_ R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCorrelation Index (CoI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTesting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTesting\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChl-a (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDensity (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenhinick (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvenness_E\u003csub\u003e2\u003c/sub\u003e (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-IBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between environmental variables and the proposed Phyto-IBI\u003c/h2\u003e \u003cp\u003eUsing the training dataset, the proposed P-IBI was found to be significantly correlated to 10 environmental variables (dissolved oxygen, total dissolved solids, conductivity, salinity, turbidity, total suspended solids, Secchi disk transparency, biological oxygen demand, ammonium, and phosphate) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Cumulative_R\u003csup\u003e2\u003c/sup\u003e and CoI of the four selected indices increased on the testing dataset, with the Phyto-IBI providing a higher correlation than the QGS in the testing dataset (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman rank correlation coefficients of the chosen indices and environmental variables using the training dataset.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eIndices\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChl a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMenhinick\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eEvenness (E2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-IBI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissolved Oxygen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.142*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.225***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.032*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.124*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Dissolved Solids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.375***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.124*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.214***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.084***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.380***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.150*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.206***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.105***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.400***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.130*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.205***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.104***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.468***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.281***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.128*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.135*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.406***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Suspended Solids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.509***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.209***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.289***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecchi Disk Transparency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.639***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.329***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.136*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.385***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological Oxygen Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.533***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.216***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.280***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmmonium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.280***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.178**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-0.141*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.192***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmmonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.144*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.376***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.240***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.171***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.074***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eOrganic Pollution Index (OPI) of the 7 Lakes of San Pablo\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the organic pollution index of the seven lakes during the wet and dry seasons. During the wet and dry seasons, Lakes Pandin and Yambo were categorized as having \u0026ldquo;less organic pollution.\u0026rdquo; Minimal variation was observed in these two lakes when the wet and dry seasons were compared. Lake Mohicap was considered to have moderate organic pollution during the wet season and less organic pollution during the dry season. Lakes Bunot, Calibato, Palakpakin, and Sampaloc were classified as having \u0026ldquo;moderate organic pollution\u0026rdquo; during the wet and dry seasons. The aquaculture-intensive lakes fall within the moderate organic pollution level, while the ecotourism lakes were categorized as having less organic pollution for both seasons. The organic pollution in the four lakes is due to relatively high levels of phosphorus and BOD in the surface water.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePhytoplankton Index of Biotic Integrity (Phyto-IBI) of the Seven Lakes of San Pablo\u003c/h2\u003e \u003cp\u003eThe resulting P-IBI for the seven lakes is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for both wet and dry season sampling. Lake Bunot had a low Phyto-IBI during the dry season and moderate Phyto-IBI during the wet season. Both wet and dry seasons show similar median values for Lake Calibato, which also translates to moderate status. Lake Mohicap had a slightly higher Phyto-IBI during the dry season compared to the wet season. A moderate Phyto-IBI was reflected in both seasons. During the dry season, Lake Palakpakin reflected a low P-IBI status, increasing to moderate in the wet season. Lake Pandin had moderate Phyto-IBI status during the dry season and good status during the wet season. In both seasons, a moderate status was reflected on Lake Sampaloc. Lastly, Lake Yambo reflected a good condition for both wet and dry. Most lakes maintained a moderate Phyto-IBI status except for Lakes Bunot and Palakpakin, which fell to a low Phyto-IBI status during the dry season. Out of the seven lakes, only Lake Yambo reflected a good Phyto-IBI condition for both the wet and dry seasons, which indicates a relatively healthy phytoplankton community. Lakes Bunot and Palakpakin show lower Phyto-IBI during the dry season, which could indicate poorer water quality or less healthy phytoplankton communities in this period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eRelationship of Phyto-IBI with environmental parameters\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eCanonical Correlation Analysis (CCA) is a multivariate ordination method used to understand the relationships between the species, environmental variables, P-IBI and OPI results. This would help reveal and provide insights into how environmental factors, P-IBI and OPI influence species. The CCA result of the wet season sampling indicated that CCA1 explains 24.14% of the variance in species metrics, while CCA2 explains 0.89%. BOD, conductivity, salinity, TDS, phosphates, TSS, and turbidity positively correlate with the CCA1 and CCA2 axes. Turbidity and phosphate have strong positive scores, indicating that higher levels are associated with the species data. ANOVA-CCA revealed that the model has a statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) relationship. DO, turbidity, TSS, and ammonia are predictors that could explain the variation of the species metrics.\u003c/p\u003e \u003cp\u003eOn the other hand, the CCA result of the dry season suggested that 19.52% of the total variation of species metrics was attributed to environmental parameters. Eight parameters (TDS, conductivity, salinity, turbidity, BOD, ammonia, ammonium, and phosphate) positively correlate with CCA1 and CCA2. Conductivity and phosphate have a strong positive correlation with the species metrics data. No statistically significant relationship exists between species metrics and environmental variables during the dry season. Both SDT and OPI are significant predictors that explain the variation of the species metrics data. The density in wet and dry seasons is close to the origin, indicating that multiple environmental variables influence this metric. Phyto-IBI during the wet and dry seasons is likely associated with nitrates.\u003c/p\u003e \u003cp\u003eThe linear regression analysis of the Phyto-IBI and OPI was significantly and positively correlated for both wet and dry seasons. In the wet season, the R\u003csup\u003e2\u003c/sup\u003e value is 0.29 with p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, while in the dry season, the R\u003csup\u003e2\u003c/sup\u003e value is 0.33 with p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eOrganic pollution in the Seven Lakes\u003c/h2\u003e \u003cp\u003eNutrients could significantly affect the growth of phytoplankton in lakes. When nitrogen and phosphorus increase due to natural and anthropogenic activities, lake water could lead to eutrophication, which causes excessive algal growth that could lead to algal blooms. Harmful algal blooms (HABs) could produce toxins harmful to aquatic organisms, and primarily, cyanobacteria are responsible for bloom occurrence. High nutrient loads can promote the production of organic matter (Huang et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The seven lakes of San Pablo are highly exposed to anthropogenic activities, which include domestic, livelihood (aquaculture), and eco-tourism. According to the study of Dimzon et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the large population and massive load of untreated domestic waste contributed to the organic pollution in Lake Sampaloc. For Lake Palakpakin, the high volume of aquaculture pens contributes to the organic pollution in the lake. Different land uses surrounding the lakes, such as plantations and rice fields, also contribute to the pollution load in the lake. Based on the organic pollution index assessment, the ecotourism lakes Pandin and Yambo were categorized as having \u0026ldquo;less organic pollution.\u0026rdquo; This is due to the fewer aquaculture activities that can contribute to the pollution load. It was also highlighted that aquaculture lakes (Bunot, Calibato, Sampaloc, and Palakpakin) had \u0026ldquo;moderate organic pollution\u0026rdquo;. Anthropogenic aquaculture activities can cause high water BOD values (Sallam and Elsayed, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Gondwe et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) found that 81\u0026ndash;90% of organic waste released during tilapia cage culture is discharged into the water body. The fish cages are also characterized by releasing nitrogen, phosphorus, and organic matter that causes nutrient enrichment, which fastens the production rate and leads to eutrophication (Lubembe et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Overall, the results showed that aquaculture activities contributed to the lakes' organic pollution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePhytoplankton Index of Biotic Integrity (Phyto-IBI) of the Seven Lakes\u003c/h2\u003e \u003cp\u003eA key step in developing the P-IBI is selecting appropriate ecological indices. Wu et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) established their P-IBI is using six metrics: chlorophyll-a, Saprobity index, Cyanobacteria index, Margalef\u0026rsquo;s index, species richness, and Menhinick index. Spatharis and Tsirtsis (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) simulated phytoplankton communities to standardize and minimize the error in the data collected and choosing the indices. In addition, the authors suggested that chlorophyll a, density, and Menhinick index were suitable for ecological quality assessment. The mentioned indices exhibited a relatively strong relationship with environmental factors. This study adopted chlorophyll a, density, Menhinick index, and Evenness E\u003csub\u003e2\u003c/sub\u003e as key indices. Phytoplankton community structure is influenced by various environmental parameters (Huszar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). High nutrients would favor phytoplankton growth, and blooms are always associated with a high load of nutrients (Xu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The resulting Phyto-IBI reflected that two (Bunot and Palakpakin) of the seven lakes recorded a \u0026ldquo;low\u0026rdquo; assessment during the dry season. The selected indices reflected the condition of the seven lakes, wherein the two lakes (Bunot and Palakpakin) are loaded with high levels of nutrients and organic matter due to aquaculture activities. Generally, the most effective indices that reflect alterations in the phytoplankton community structure are productivity and species density (Devlin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In the study of Zhu et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in Lake Gehu, it was found that Phyto-IBI in the dry season was higher compared to the wet season. These authors claimed that increased temperature during the summer leads to increased cyanobacteria and decreased water quality. However, this study showed that most lakes have lower Phyto-IBI values during the dry season. This might be due to the high phytoplankton density and chlorophyll level that affected the Phyto-IBI value. The diversity during the dry season is relatively low due to the dominance of some phytoplankton.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRelationship of Phyto-IBI with environmental variables\u003c/h2\u003e \u003cp\u003eSeveral studies have found that Phyto-IBI showed high sensitivity to water quality deterioration. A study by Zhu et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) indicated that Phyto-IBI and its constituent parameters were highly correlated with water quality factors. Their study has found that the main water quality parameters affecting the Phyto-IBI were total nitrogen, permanganate index, chlorophyll, and dissolved oxygen. Nutrient concentrations significantly affect the phytoplankton community because an elevated concentration could lead to eutrophication. This could cause water quality degradation due to harmful algal blooms. High nutrients correspond to lower Phyto-IBI scores, which indicates poor ecological health (Smith et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In San Pablo, aquaculture lakes (Bunot, Calibato, Palakpakin, and Sampaloc) tend to have higher nutrient concentrations, leading to lower Phyto-IBI values. Another water parameter that could impact the Phyto-IBI result is temperature. Reynolds (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) indicated that temperature affects the metabolic rate of phytoplankton and seasonal distribution. Warmer temperature increases growth rates that could favor the dominance of less desirable or toxic species. This could lower the Phyto-IBI value. The study of Cao et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that electrical conductivity (EC) had a significant positive correlation with Phyto-IBI values for both wet and dry seasons in Lake Dianchi. The study by Wu et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in the Lake Taihu Basin (LTB) exhibited a positive correlation between Phyto-IBI and the Water Quality Index (WQI) on all sites. This would justify that water quality influenced the Phyto-IBI since it impacts the growth, composition, and distribution of phytoplankton communities in aquatic ecosystems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eImportance of Developing Multi-metric Index for management\u003c/h2\u003e \u003cp\u003eThe multi-metric approach or index of biotic integrity (IBI) was originally developed by Karr (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). It was the most common indicator used to characterize stream conditions. This has also been criticized because multi-metric indices reduced the data into a single dimensionless number. However, Gerritsen (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) argued that the goal of IBI is for data simplification, and this feature is advantageous for resource managers who are not experts in the field. This could also provide an easy understanding of the current status rather than presenting large amounts of data to be interpreted. This study supported the claim of Gerritsen (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and other IBI studies that using a multi-metric index in biological data could provide a reliable assessment of ecosystem quality. Most researchers believe that developing an index of biological integrity has certain limitations in evaluating ecological health. However, it is still an effective evaluation method (Zhu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Ruaro and Gubiani (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) suggest that simple indices are ideal for the rapid and efficient monitoring of freshwater environments, particularly in developing countries such as the Philippines, where research on P-IBI is still lacking. The Phyto-IBI would provide a more comprehensive approach to evaluating ecosystem health.\u003c/p\u003e \u003cp\u003eWith the observed results of low Phyto-IBI for two lakes (Bunot and Palakpakin) during the dry season, efforts must be made by the local government unit of San Pablo to improve its condition by reducing the nutrient load in the water. This could prevent phytoplankton blooms from deteriorating the lake ecosystem. Lakes Yambo and Pandin are the ideal lakes for the P-IBI values. It reflected good ecological health. The LGU can use the result of P-IBI to identify impaired lakes that need remediation. It can also provide scientific data for policy development since it can offer an objective basis for establishing regulations and making resource management decisions to improve and protect environmental quality.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe development of Phytoplankton Index of Biotic Integrity (Phyto-IBI) for the seven lakes of San Pablo is an effective tool for assessing the condition of the lake ecosystem. It can be a comprehensive ecosystem health indicator integrating biological and environmental factors. The identified metrics for developing the Phyto-IBI include the chlorophyll a, density, Menhinick index, and Evenness E2 (Pielou) index. Based on the calculated Phyto-IBI, it showed that the aquaculture lakes (Bunot, Calibato, Sampaloc, and Palakpakin) had lower Phyto-IBI compared to the ecotourism lakes (Mohicap, Pandin, and Yambo). It also reflected that a lower Phyto-IBI was observed for most of the lakes during the dry season. The aquaculture lakes were classified as \u0026ldquo;moderate\u0026rdquo; during the wet and dry seasons. However, two lakes (Bunot and Palakpakin) were classified as \u0026ldquo;low\u0026rdquo; Phyto-IBI during the dry season. Lake Yambo was classified as \u0026ldquo;good\u0026rdquo; for both seasons, which could be the ideal or model lake for all seven lakes. The OPI indicated that the four aquaculture lakes have \u0026ldquo;moderate organic pollution\u0026rdquo; for both seasons, while the three other ecotourism lakes had \u0026ldquo;less organic pollution.\u0026rdquo; Among the environmental parameters, CCA revealed that TSS, turbidity, phosphorus, TDS, conductivity, salinity, and BOD significantly influenced the variation of the chosen indices. Linear regression analysis showed that Phyto-IBI and OPI were significantly and positively correlated for both seasons. With these perceived outcomes, the LGU can use it to establish regulations and make resource management decisions to improve and protect the ecosystem health of the seven lakes of San Pablo.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research team expresses its deep appreciation to the Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development (PCAARRD), Department of Science and Technology (DOST) for their invaluable support under the project \u0026ldquo;Development of Models for the Assessment and Monitoring of the Seven Lakes of San Pablo\u0026rdquo;.\u0026nbsp;We are also sincerely grateful to the LGU of San Pablo City, FARM-C, and the fisherfolk of each lake for their vital assistance during the study. Additionally, we extend our heartfelt thanks to Mr. Lawrence Victor Vitug, the Project Technical Assistant, for his expertise in identifying the phytoplankton species.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was conducted under the project Development of Models for the Assessment and Monitoring of the Seven Lakes of San Pablo (Project No. N92732A), which was and funded by the Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development (PCAARRD), Department of Science and Technology (DOST).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study\u0026nbsp;was conducted without involving human participants, animals, or sensitive personal data. Therefore, the standard ethical considerations related to the protection of human and animal subjects, informed consent, privacy, and confidentiality are not applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthorship Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eJohn Vincent R. Pleto\u003c/strong\u003e: Conceptualization of the research, Methodology (Data Collection), Data Visualization and Analysis, Investigation, Writing \u0026ndash; original draft, Formal Analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMayzonee D. Ligaray\u003c/strong\u003e: Conceptualization of the research, Writing \u0026ndash; review and editing of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrancis S. Magbanua\u003c/strong\u003e: Conceptualization of the research, Methodology, Writing \u0026ndash; review and editing of the manuscript, Formal Analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-asadi, A. A., \u0026amp; Al-Hejuje, M. M. 2019. Application of Organic Pollution Index (OPI) to assess the water quality of Al-Chibayish marsh, southern Iraq. \u003cem\u003eMarsh Bulletin\u003c/em\u003e 14(1) 57-64.\u003c/li\u003e\n\u003cli\u003eBlanco, S., B\u0026eacute;cares, E., Cauchie, H.M., Hoffmann, L., Ector, L., 2007. Comparison of biotic indices for water quality diagnosis in the Duero Basin (Spain). \u003cem\u003eArch. Hydrobiol\u003c/em\u003e. 17, 267\u0026ndash;286. \u003c/li\u003e\n\u003cli\u003eCao, J.-L., Liang, H.-Y., Zhang, Y.-H., Du, S.-L., Zhang, J., \u0026amp; Tao, Y. (2024). 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Development and testing of the phytoplankton biological integrity index (P-IBI) in dry and wet seasons for Lake Gehu. \u003cem\u003eEcological Indicators\u003c/em\u003e, 129, 107882. https://doi.org/10.1016/j.ecolind.2021.107882. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bioindicator, Organic Pollution Index (OPI), index of biotic integrity, seasonal variation","lastPublishedDoi":"10.21203/rs.3.rs-4916576/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4916576/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhytoplankton Index of Biotic Integrity (Phyto-IBI) is a multi-metric index designed to simplify extensive datasets into a single dimensionless value that could assess ecosystem health. The Seven lakes provide various ecosystem services from which different stakeholders\u0026rsquo; benefit. However, these lakes are continuously experiencing disturbance because of anthropogenic activities. This research aimed to develop a P-IBI and Organic Pollution Index (OPI) for the seven lakes. P-IBI was developed using 21 ecological phytoplankton indices. The cumulative_R\u003csup\u003e2\u003c/sup\u003e and correlation index were used to select the determining indices for the final P-IBI. Canonical Correlation Analysis (CCA) was conducted to test the relationship between the metrics and P-IBI and the environmental variables. The results indicated that aquaculture lakes had lower P-IBI and OPI compared to ecotourism lakes. The four aquaculture lakes were categorized as having \u0026ldquo;moderate\u0026rdquo; P-IBI and OPI levels. During the dry season, lakes Bunot and Palakpakin were classified as having \u0026ldquo;low\u0026rdquo; P-IBI. Lake Yambo, recognized as having the best environmental conditions among the lakes, was classified as \u0026ldquo;good\u0026rdquo; P-IBI. Regarding seasonal variation, the P-IBI is generally lower during the dry season for most of the lakes. CCA revealed that several parameters significantly influenced the variation of the indices during the wet and dry seasons. In addition, regression analysis showed a positive correlation between OPI and P-IBI. These findings imply that P-IBI is indeed impacted by water quality. Based on the results, P-IBI and OPI may serve as indicators of the ecological health of the seven lakes of San Pablo. The local government may establish regulations and make informed resource management decisions based on the study results to improve and protect the lake ecosystem.\u003c/p\u003e","manuscriptTitle":"Evaluating Ecosystem Health of the Seven Maar Lakes of San Pablo City using the Phytoplankton Index of Biotic Integrity (Phyto-IBI)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-26 09:39:39","doi":"10.21203/rs.3.rs-4916576/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22c60338-ab09-4345-98ae-eacd0f47db51","owner":[],"postedDate":"September 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-10T12:08:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-26 09:39:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4916576","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4916576","identity":"rs-4916576","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}