Three different modeling approaches employing MaxEnt were performed in this study points this: i expansion of the localized fundamental niches of the three myxomycetes species, ii isoth
INTRODUCTION
Research rationale
The Anthropocene era presents significant environmental challenges, primarily driven by climate change and anthropocentric activities like urbanization and industrialization While these developments have exacerbated global environmental issues, they have also contributed to progress in developing nations, such as the Philippines.
The Philippines, an archipelago with the richest biodiversity in Southeast Asia, has a wealth of indigenous flora and fauna, yet the major microbial communities essential for agricultural and environmental processes remain underexplored The country's coastline is subject to complex natural processes that lead to both long-term and short-term changes, primarily driven by littoral transport, which moves eroded materials along beaches through waves and currents, resulting in shoreline alterations These coastal ecosystem changes significantly impact human life, infrastructure, land, and the socio-economic value of coastal areas Additionally, human activities during the Anthropocene have severely affected many coastal habitats in Southeast Asia, leading to issues such as eutrophication and chemical pollution.
To address the impact of anthropocentric activities on wetland landscapes and indigenous microbial flora, it is essential to implement policy recommendations that promote sustainable environmental management grounded in scientific research.
5 based evidence using the aforementioned subjects are still considered in their infancy
Mapping and modeling techniques offer valuable visuals for predicting the distribution of biological resources, tracking the spread of plant pathogens, and understanding land use changes These visuals are essential for developing effective management strategies aimed at conserving forest ecosystems, enhancing agricultural practices, and guiding urban development in the Philippines.
This Bachelor’s thesis focuses on key themes related to environmental management in the Anthropocene It is organized into three independent yet interconnected studies that employ maps and models to explore specific questions and hypotheses, addressing significant research gaps in the field.
Research questions and hypotheses
1.2.1 Maxent modeling of three bright-spored species
The Philippines leads Southeast Asia in documenting plasmodial slime molds (myxomycetes), with a total of 162 species recorded (Macabago et al 2020) Recent surveys have revealed that major terrestrial ecosystems in the country support a rich diversity of myxoflora (Bernardo et al 2018) Additionally, there is notable variation in the diversity of myxomycetes species collected from various substrates in key conservation areas across the Philippines.
Research indicates significant differences in myxomycete assemblages (Macabago et al 2017; Pecundo et al 2017; Dagamac et al 2017) To date, only one study has employed species distribution modeling (SDM) for myxomycetes in the Philippines Almadrones-Reyes and Dagamac (2018) identified suitable habitats for the common dark-spored myxomycete, Diderma hemisphaericum, and predicted its range expansion across other Philippine islands under two climate change scenarios (A2 and B1) Despite the numerous myxomycete species reported in the country, there has been no attempt to predict the geographical niches suitable for bright-spored myxomycetes.
Question: Using maxent modeling, what are the probable suitable habitats for the three selected bright-spored myxomycetes species?
Hypothesis: The bioclimatic factor influences the determination of the expanding range shifts of putative suitable habitats where the three selected bright-spored myxomycetes will thrive
1.2.2 Peronosclerospora philippinensis (downy mildew) in the Philippines
Background: The Philippines' maize-growing agricultural industry has been plagued for a long time by downy mildews, more specifically by its causal pathogen,
Peronosclerospora philippinensis was first recorded by Baker in 1916, as noted by Exconde (1982), but the most thorough documentation of the disease in the Philippines was conducted by Weston in 1920 Over the past few decades, there has been a significant increase in disease occurrence, particularly in Northern Luzon and various regions of Mindanao.
Seven key agricultural techniques have been employed to combat the spread of the pathogenic disease caused by P philippinensis, the most virulent member of the downy mildew family This pathogen leads to significant economic losses in corn production, with crop yields decreasing by approximately 40-60% during outbreaks However, existing records of the disease are largely anecdotal, and comprehensive distribution and risk maps for various plant pathogens in the country remain unavailable.
Question: What is the possible distribution of the pathogen, Peronosclerospora philippinensis, under different climate change scenarios?
Hypothesis: Similar to other fungal-like protist allies, these pathogens will have an expanding range shift under different climate change storylines
1.2.3 LULC of urban coastline of Metro Manila
The urban coastline of Metro Manila is a heavily polluted environment, crucial for its international port, extensive fishing areas, and aquaculture sites for oysters and mussels Historically, the bay has been a hub of socio-economic growth since pre-Hispanic times, providing vital natural resources that support local communities However, rapid population growth and industrialization in the watershed have led to a significant decline in water quality, resulting in increased hypoxia, anoxia, toxic microalgae blooms, and chronic red tides caused by dinoflagellates.
Manila Bay is significantly impacted by urban emissions, particularly excess nutrients like nitrogen and phosphorus, as well as heavy metals, making it a major marine pollution hotspot in the East Asian Seas (Chang et al., 2009; Maria et al., 2009) Addressing these environmental challenges requires effective and sustainable management strategies.
Question: What are the major changes in the urban coastline of Metro Manila in the span of 30 years (1992-2020)?
Hypothesis: A clear change in the coastal land use in Metro Manila’s Bay over the last
Research objectives
1.3.1 Maxent modeling of three bright-spored species
● To show the possible suitable habitats and distribution of the three bright- spored species in the Philippines
● To create a predictive distribution map of the three bright-spored species using three different model approaches
1.3.2 Peronosclerospora philippinensis (downy mildew) in the Philippines
● To produce maps that visualize the distribution of Peronosclerospora philippinensis species in the Philippines under different climate change scenarios
1.3.3 LULC of urban coastline of Metro Manila
● To create a map that shows the changes in the land use/land cover around the coastal area of Metro Manila
● To show the major changes that happen in the coastline in the last 40 years
Scope and limitations
This study addresses three key Anthropocene issues: first, it evaluates the potential habitat suitability for three selected bright-spored myxomycete species in the Philippines; second, it updates the distribution of a plant pathogen in the context of changing climate scenarios; and third, it examines land use and land cover changes along the coastline of the urban capital of the Philippines.
This study aims to tackle environmental management challenges in the Anthropocene by utilizing two key visualization techniques: predictive models generated through the MaxEnt algorithm and land use/land cover (LULC) classification maps created with ArcGIS software.
This study's findings are based solely on computer-generated in silico analysis, as field ground-truthing and site validation were not conducted due to restrictions imposed by the Philippine local government during the research period.
Definition of terms
Bioclimatic variables are commonly used in species distribution modeling and similar ecological modeling approaches to represent annual trends, seasonality, and extreme or limiting environmental circumstances
Interactive Supervised Classification is an ArcMap tool that speeds up classification that includes all the bands available in the image layer selected
International Union for Conservation of Nature (IUCN) is an international organization dedicated to the conservation of nature and the sustainable management of natural resources
PhilGIS is a website where you can access and download different Philippine geospatial data
LITERATURE REVIEW
Myxomycetes
Myxomycetes, a unique group of 998 species found worldwide, straddle the kingdoms of Plantae and Animalia Although they are often located in environments similar to fungi, they are classified as a distinct taxon within the Fungi kingdom (Baba & Sevindik, 2018) A phylogenetic study analyzing highly conserved 1-alpha (EF-1α) gene sequences has demonstrated that myxomycetes are not fungi (Baldauf & Doolittle).
The life cycle of myxomycetes includes two distinct trophic stages: uninucleate amoebae and a multinucleate plasmodium, which consumes bacteria, fungi, and other microorganisms (Baba, 2012) Bacteria are the primary nutrient source for myxomycetes, although they also feed on fungi and organic matter (Baba & Sevindik, 2018; Ergul et al., 2005) As myxomycetes progress in their life cycle, the engulfed bacteria and fungi can develop mutualistic relationships with them (Cohen, 1941) Their presence is often associated with decaying or living plant material in terrestrial forest ecosystems, where factors such as humidity, temperature, light intensity, pH, and environmental degradation significantly influence their diversity and abundance.
12 and insects have also been considered Environmental pollution and the rise in toxins also reduce the diversity of Mycetozoa (Ko et al., 2011)
Myxomycetes, commonly known as slime molds, are unique protists that produce macroscopic fruiting bodies, making them easy to capture and classify (Stephenson & Stempen, 1994) These organisms are significant for their ability to accumulate high metal concentrations in their cells, similar to fungi (Keller & Everhart, 2010) Slime molds consume bacteria and microorganisms while providing habitats for various fungi and insects, particularly beetles and flies, with some beetle species relying on slime mold spores and plasmodia for nutrition (Stephenson & Stempen, 1994) Myxomycetes are widely distributed across diverse habitats, including temperate forests, tropical rainforests, dry land ecosystems, and the tundra of northern Siberia (Kazunari, 2010; Takahashi & Hada, 2009; Dagamac, 2012) Research indicates that myxomycete abundance is highest in grassland and agricultural soils (Feest & Madelin, 1985), yet urbanization has disrupted their ecology, with limited studies on urban myxomycetes (Ing, 1998) Despite disturbances like forest fragmentation and habitat loss, myxomycetes show resilience, although their diversity has declined in areas such as the Amazon rainforest (Rojas & Stephenson, 2013).
Species Distribution Modeling (SDM)
Modeling the potential distribution of macroecological organisms, including ephemeral species like fungi, has been extensively explored in literature (Cabral et al 2017; Connolly et al 2017; Guisan and Rahbek 2011) Species distribution models (SDMs) are increasingly recognized as valuable tools in fungal research, yet most studies have focused on macrofungi, lichens, and fungal pathogens (Hao et al 2020; Sato et al 2020; Dymytrova et al 2016) There is a notable lack of SDM studies on fungus-like protists, particularly myxomycetes, which are significant microbial predators in soil ecosystems Myxomycetes, characterized by their macroscopically visible fruit bodies and predominantly sexual reproduction (Feng et al 2016), are classified within the Amoebozoa and divided into two clades: Lucisporomycetidae and Columellomycetidae (Adl et al 2012, 2018; Ruggiero et al 2015).
Myxomycetes can be classified into two subdivisions: dark-spored and bright-spored, based on the color of their spore mass This classification considers various factors, including the morphological characteristics of fructification, as well as the appearance of the plasmodium and the growth of the fruiting body (Ross 1973).
2.3 Land use/ Land cover classification using remote sensing and its application to coastline studies
Thematic data essential for GIS analysis, particularly regarding land use and land cover, is primarily obtained through remote sensing techniques Landsat satellite imagery and aerial photography are frequently utilized to evaluate land cover distribution.
During the late twentieth and early twenty-first centuries, developing nations experienced significant land-use and land-cover (LULC) changes due to rapid and uncontrolled population growth alongside industrialization.
Land Use and Land Cover (LULC) change plays a crucial role across various sectors that depend on Earth observations, such as urban planning, environmental vulnerability assessments, and the monitoring of natural disasters It is increasingly acknowledged as a significant factor driving environmental changes The ongoing challenge lies in balancing the preservation of the natural environment with the enhancement of economic and social benefits derived from land use.
Recent advancements in remote sensing technologies have significantly enhanced our ability to analyze land use and land cover (LULC) changes Over the past two decades, researchers have focused on developing accurate methods for LULC classification and monitoring temporal changes (Manandhar et al 2009) Satellite imagery offers the benefits of multi-temporal data and extensive spatial coverage, making it a valuable resource for LULC mapping Studies utilizing medium- and low-resolution satellite observations, such as Landsat, MODIS, IRS, ASTER, and SPOT, have been instrumental in mapping, monitoring, and predicting LULC trends (Mas et al 2017; Wentz et al 2008; Toure et al 2018; Usman et al 2020; Stefanov & Netzband, 2005).
Monitoring changes in land use/land cover (LULC) is crucial for addressing environmental issues and promoting sustainability, as it enhances our understanding of environmental transformations The rapid global population growth and economic activities have led to urbanization and significant shifts in LULC due to construction and land development Effective LULC monitoring is essential for creating sustainable urban plans, particularly in coastal areas, to improve future urban development and management.
MATERIALS AND METHODS
Updating the potential Philippine distribution of the maize pathogen, Peronosclerospora
Geographical coordinates of pathogen occurrences were obtained from three distinct platforms The first source, grey literature, encompasses anecdotal reports and scientific studies that document the disease's emergence in corn For historical records lacking original geographical data, the geographic centroid was estimated through triangulation of the nearest known locality where corn is cultivated.
The study focuses on the distribution of P philippinensis in the Philippines, utilizing an electronic database for accurate geographic coordinates and personal anecdotes for data verification A total of 12 geographic coordinates were confirmed and converted into a CSV file, which was then overlaid on a Philippine map using ArcGIS 10.3 Current climatic conditions were analyzed through 19 bioclimatic variables sourced from the PhilGIS web portal, with a raster resolution of 1km Future climate projections for 2080 were obtained from the GCM downscaled data portal, employing the CICRO model from the CMIP5 assessment Two scenarios were considered: the A2 storyline predicts a 3.4° Celsius increase due to higher carbon emissions, while the B1 storyline forecasts a 1.8° Celsius rise with more efficient technology These scenarios were converted to ASCII format for use in MaxEnt software.
The software MaxEnt (maximum entropy species modeling), Version 3.4.4, was utilized to map the potential geographic distribution of P philippinensis in the Philippines based on 12 identified coordinates MaxEnt was chosen for its advantages, including the ability to handle present-background data, effectiveness with low occurrence data, and robust model production through maximum entropy distribution estimation To enhance model predictive ability and prevent overfitting, ENMeval was employed, which suggested adjustments to the regularization multiplier and feature type for various model runs The model settings included a convergence threshold of \(10^{-5}\), a maximum of 5000 iterations, and 10,000 background points A Jackknife test assessed the relative importance of selected variables, while model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC), with AUC values ranging from 0 to 1, indicating prediction suitability Response curves were analyzed to explore the relationships between bioclimatic variables and predicted probabilities.
The presence of P philippinensis was analyzed using MaxEnt, which generated an output file in ASCII format This file was then imported into ArcMap 10.4 for detailed visualization In ArcMap, the data was classified into four distinct categories using defined intervals.
Land use land cover change and coastline change detection of the urban coastline in Metro Manila, Philippines
Metro Manila, the national capital region of the Philippines, is the country's main tourist destination and serves as its commercial, educational, and entertainment center Despite being the smallest region, it is the most densely populated, with 16.3 million residents, making it the 11th most populous region globally Situated on Luzon Island and bordered by Bulacan, Rizal, Laguna, and Cavite, the study focuses on the western side of Metro Manila, which is closer to the coastline, to analyze urban coastline changes, excluding the eastern side.
Figure 1 illustrates the geographical context of Metro Manila within the Philippines, highlighting its location and the surrounding provinces Additionally, it features a Landsat map that focuses on Metro Manila and the designated study area, providing a comprehensive view of the region's landscape For further details, refer to the source at EarthExplorer.
3.3.2 Gathering of maps and data
Four Landsat datasets were obtained from the United States Geological Survey (USGS) EarthExplorer to monitor changes along the urban coastline of Metro Manila To ensure accurate classification, images with minimal cloud cover were selected for each year The dataset comprises two images from the Landsat 5 Thematic Mapper (TM) sensor for the years 1992 and 2005, along with two images from the Landsat 8 Operational Land Imager (OLI) sensor for the years 2014 and 2020.
1) Moreover, the vector data that was used to clip the study area were downloaded from DIVA-GIS (https://www.diva-gis.org/)
Table 1 Detailed information of the datasets used in this study
Satellite Sensor No of Bands Acquisition Date Spatial Resolution (m)
The vector data for Metro Manila was extracted from the Philippines' vector dataset obtained from DIVA-GIS to create a map of the Urban Coastline Subsequently, this clipped vector data was utilized to mask and extract Landsat images for each dataset using the "Extract by Mask" tool in ArcToolbox.
ArcMap 10.8 software was utilized to create the study area map essential for classification Finally, all annual maps were completed prior to the classification process.
This study employed supervised classification to analyze the dataset for each year, where users select pixels with similar properties to define specific classes Using ArcMap 10.8, the software automatically identifies pixels that match the assigned class properties (Masria et al., 2020) Four distinct classes were established for the research area, as detailed in Table 2 The Maximum Likelihood method was applied to perform supervised classification on the four images, utilizing bands 4, 3, and 2 for Landsat 8, and bands 3, 2, and 1 for Landsat.
5 were used to show the natural color of the images for better classification
Table 2 Land cover classes used in the study and their definition (Source: https://www.arcgis.com)
Water areas are characterized by the consistent presence of water year-round, excluding regions with temporary or sporadic water sources These areas typically feature minimal vegetation, lack rock outcrops, and do not include man-made structures such as docks.
Grass open areas are characterized by uniform grasses and minimal taller vegetation, featuring wild cereals and grasses without clear human cultivation Examples include natural meadows, fields with sparse tree cover, open savannas, parks, golf courses, lawns, and pastures.
Trees are defined as significant clusters of tall vegetation, typically reaching heights of 15 meters or more, characterized by a closed or dense canopy Examples include wooded areas, dense vegetation clusters within savannas, plantations, swamps, and mangroves, where the thick canopy may obscure underlying water sources.
Human-made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings, and residential housing; examples: houses, dense villages/towns/cities, paved roads, asphalt
After assigning the correct classes to the images, Interactive Supervised Classification was employed to classify the entire dataset The classification output was then assessed for accuracy by comparing the classes with Landsat data Following the evaluation and validation of the classification results, the data was converted into a polygon format using ArcToolbox's "Raster to Polygon" tool.
The Polygon tool allows for the determination of area sizes for each class by converting data into polygon or vector formats and utilizing the field calculator in the attribute table of categorized layers After converting classified images from raster to vector as polygons, these features can be transformed into line features using ArcGIS/ArcToolbox The coastline length is then calculated using the field calculator in each layer's attribute table, and this method is consistently applied to obtain coastline lengths across multiple years.
Accuracy assessment is the final step in the remote sensing data process, crucial for evaluating how accurately pixels are classified into land cover categories (Anand, 2017) For effective accuracy evaluation, priority is given to locations visible on high-resolution Landsat images, Google Earth, and Google Maps (Rwanga & Ndambuki, 2017) A map generated from remotely sensed or spatial data cannot be deemed complete without verifying its accuracy (Anand, 2017) An effective accuracy assessment requires a comparison between an interpreted map or classified image derived from remote sensing data and another reliable data source.
To ensure accurate classification results, it is essential to utilize a reference map, high-resolution images, or ground truth data Following the classification process, it is crucial to assess the degree of error in the final output, which reflects the identified categories on the map Errors typically arise from incorrect pixel labeling for specific categories (Anand, 2017).
Overall accuracy, defined as the ratio of correctly identified pixels to the total number of pixels tested, is a common metric for assessing mapping accuracy However, it overlooks off-diagonal elements and makes it challenging to compare different accuracy levels when varying numbers of accuracy sites are used The error matrix also facilitates the calculation of producer and consumer accuracies, which reflect the errors of class producers and users These basic accuracy measurements can yield results influenced by random pixel classification, lacking a statistical comparison method To address this, Kappa analysis is recommended, as it incorporates off-diagonal factors based on row and column marginal totals This discrete multivariate technique evaluates classification accuracy from an error matrix, producing a kappa coefficient or Khat statistic that ranges from 0 to 1.
To achieve a precise assessment of the entire image across all categorized classes, it is essential to calculate the overall accuracy, which reflects the ratio of correctly identified pixels This metric serves as a comprehensive indicator of the map's accuracy for all classes The overall accuracy can be determined using a specific equation.
RESULTS AND DISCUSSION
Updating the potential Philippine distribution of the maize pathogen, Peronosclerospora
PERONOSCLEROSPORA PHILIPPINENSIS (DOWNY MILDEW), USING A PREDICTIVE MACHINE LEARNING
The species distribution models for Peronosclerospora philippinenses illustrate its localized distribution and identify predictive suitable habitat areas under current conditions and two climate scenarios (A2 and B1) These models, generated using the maximum entropy algorithm, are displayed as heat maps that reflect the calculated probability of occurrence.
Dark Green indicates low suitability (0-