Using maps and models as a tool for conservation and management in the age of the anthropocene pieces of evidence from indigenous protists and a local landscape of the philippine archipelago

THAI NGUYEN UNIVERSITY UNIVERSITY OF AGRICULTURAL AND FORESTRY JAMES EDUARD L DIZON USING MAPS AND MODELS AS A TOOL FOR CONSERVATION AND MANAGEMENT IN THE AGE OF THE ANTHROPOCENE: PIECES OF EVIDENCE FROM INDIGENOUS PROTISTS AND A LOCAL LANDSCAPE OF THE PHILIPPINE ARCHIPELAGO BACHELOR THESIS Study Mode: Full-time Major: Environmental Science and Management Faculty: International Programs Office Batch: K49 – AEP Thai Nguyen, 10/22/2021 DOCUMENTATION PAGE WITH ABSTRACT Thai Nguyen University of Agriculture and Forestry Degree Program Bachelor of Environmental Science and Management Student name James Eduard L Dizon Student ID DTN1754290033 Using maps and models as a tool for conservation and Thesis Title management in the age of the Anthropocene: Pieces of evidence from indigenous protists and a local landscape of the Philippine archipelago Supervisor (s) Dr Duong Van Thao & Dr Nikki Heherson A Dagamac Abstract: Three independent yet cohesive topics that utilize maps and models to address the gaps in major Anthropocene issues related to environmental management in the Philippines is employed for this thesis The first study reported potential suitable geographical distributions of three different bright-spored myxomycetes namely, Arcyria cinerea, Perichaena depressa, and Hemitrichia serpula 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) isothermality (BIO3) is the most influential bioclimatic predictor, and (iii) models developed in this study can serve as a useful baseline to enhance the conservation efforts for most habitats in the country that are directly affecting microbial communities due to rampant habitat loss and rapid urbanization The second study of this thesis performed simple bioclimatic modeling to update the anecdotal reports of the disease-causing pathogen on our common maize plants, Peronosclerosopora philippinensis ii The correlative modeling also performed in this study showed the following: (i) mean diurnal temperature (BIO2) affects the ecological distribution of the disease, (ii) range expansion on other plantations of the country, and (iii) suggest potentialities on places where the species is most likely to infect The last component of this thesis utilizes remote sensing technology to cover the urban coastline of Metro Manila Interestingly, this component has yielded the following results: (i) between 1992 and 2020, shoreline changes have been detected within approximately 1.5 km decreased, (ii) The northern part of the study area, which shifted from being composed of trees and grasslands to now enormous fishponds, and (iii) the critically important Ramsar site, LPPCHEA, have maintained the preservation of its natural mangrove forest Overall, this Bachelor’s thesis has shown how maps and models can be used in creating narratives that can address interconnected environmental issues However, despite these advantages, this new mode of visuals should always be treated with caution and utmost critical interpretations Nevertheless, in silico/computer-assisted studies is the modern approach that can be used by future environmental scientists and managers to address pressing issues in this era of the Anthropocene Keywords: conservation, machine learning, maximum entropy, niches, urbanization Number of pages 78 Date of October 22, 2021 Submission: iii ACKNOWLEDGEMENT ● Firstly, I would like to thank MY FAMILY (Papa, Mama, Kuya, and Miggy) for all the support they have given me throughout my thesis and my journey in my academic life I wouldn’t accomplish all of this without them ● To my thesis supervisors, Dr Nikki Heherson A Dagamac and Dr Duong Van Thao, a big thanks for helping and guiding me in conducting my thesis ● To Dr Sittie Aisha B Macabago of the University of Arkansas, Fayetteville, USA, thank you for the help that you gave during my thesis especially on MaxEnt modeling of the bright-spored myxomycetes ● To Dr Reuel M Bennett of the University of Santo Tomas, Manila, Philippines thank you for sharing your knowledge on the oomycete pathogens, Peronosclerospora philippinensis ● To the AEP Family, thank you for the help, support, understanding, updates, and for answering all the questions about the thesis ● My Vietnam family/friends, Henry, Raphael, Isaiah, JC, Ella, Angel, Elisha, Jemimah, Ronnieca, Hanna for your continuous love and support ● To my friends, Dale, Elmo, Austin, Marc, Francis, Noehl for your understanding and support ● To King for being there when I needed his help and guidance ● To my mentor/life coach/adviser/brother, thank you for all the lessons that you have taught me and all the advice that you gave me that helped me in accomplishing the things that I never thought I would be able to Thank you for believing in me and trusting my abilities, and for seeing the best in me even when I don't believe it myself ● To all who helped during the process of my thesis from the planning, brainstorming, and up until the very last step, Thank you! To all of those who supported and believed in me, all the stress, the hard work, the headache paid off Thank you very much, I appreciate it all iv This Bachelor’s Thesis is dedicated to my family for their neverending love and support My Father, Eric M Dizon My Mother, Marilou L Dizon And my two brothers, Eric Jason L Dizon & Jericho Miguel L Dizon You have been my source of inspiration throughout my academic life Your love and support have been my strength during the hard times and because of all of you, I made it v TABLE OF CONTENTS List of Figures List of Tables List of Abbreviations CHAPTER I INTRODUCTION 1.1 Research rationale 1.2 Research questions and hypotheses 1.2.1 Maxent modeling of three bright-spored species .5 1.2.2 Peronosclerospora philippinensis (downy mildew) in the Philippines 1.2.3 LULC of urban coastline of Metro Manila 1.3 Research objectives 1.3.1 Maxent modeling of three bright-spored species .8 1.3.2 Peronosclerospora philippinensis (downy mildew) in the Philippines 1.3.3 LULC of urban coastline of Metro Manila 1.4 Scope and limitations .9 1.5 Definition of terms 10 CHAPTER II LITERATURE REVIEW 11 2.1 Myxomycetes 11 2.2 Species Distribution Modeling (SDM) 13 2.3 Land use/ Land cover classification using remotes sensing and its application to coastline studies .14 CHAPTER III MATERIALS AND METHODS 16 3.1 Maxent modeling for the prediction of the suitable local geographical distribution of selected bright spored myxomycetes in the Philippine archipelago 16 3.1.1 Occurrence data and environmental layers .16 3.1.2 Modeling procedure 17 3.2 Updating the potential Philippine distribution of the maize pathogen, Peronosclerospora philippinensis (downy mildew), using predictive machine learning approach 19 3.2.1 Data Gathering 19 3.2.2 Model performance and calibration 21 3.3 Land use land cover change and coastline change detection of the urban coastline in Metro Manila, Philippines .22 3.3.1 Study Area 22 3.3.2 Gathering of maps and data 24 vi 3.3.3 Processing of images 24 3.3.4 Classifying the data 25 3.3.5 Accuracy Assessment 26 CHAPTER IV RESULTS AND DISCUSSION 29 4.1 Maxent modeling for the prediction of the suitable local geographical distribution of selected bright spored myxomycetes in the Philippine archipelago 29 4.1.1 Results 29 4.1.2 Discussion 36 4.2 Updating the potential Philippine distribution of the maize pathogen, Peronosclerospora philippinensis (downy mildew), using predictive machine learning approach 41 4.2.1 Results 41 4.2.2 Discussion 42 4.3 Land use land cover change and coastline change detection of the urban coastline in Metro Manila, Philippines .44 4.3.1 Results 44 4.3.2 Discussion 50 CHAPTER V SUMMARY AND CONCLUSION .53 REFERENCES .56 APPENDICES 68 vii List of Figures Figure A) The map of the Philippines shows the location of Metro Manila B) Metro Manila and the provinces surrounding it C) Landsat Map showing Metro Manila and the chosen study area .23 Figure Occurrence points of three bright-spored species in the Philippines based on the published geographic coordinates of species occurrences where each of the three bright-spored species was recorded 31 Figure Results area under the curve (AUC) analysis, including mean AUC values for each bright-spored species obtained using the three model approaches 33 Figure Species distribution models for the three bright-spored species of myxomycetes showing a map of the Philippines and the predictive suitable habitat areas under the three model approach generated by maximum entropy algorithm The maps were presented on a heat map based on the calculated probability of occurrence for the three bright-spored species 35 Figure Species distribution models for the localized distribution of Peronosclerospora philippinenses and the predictive suitable habitat areas under the current and two climate storylines (A2 and B1 scenarios) generated by maximum entropy algorithm The maps were presented on a heat map based on the calculated probability of occurrence .41 Figure A) Map of Metro Manila showing the location of LPPCHEA in a thick red box B) An enlarged map that shows the location of LPPCHEA inside a thick red box 46 Figure Land Use Land Cover change map from 1992-2020 of the Urban coastlines of Metro Manila .47 Figure Overview of the major changes that happened in the urban coastline of Metro Manila A) Map of Metro Manila that shows the part of the coastline that has been changed (Source: Google Earth Pro) In thick black boxes are the highlighted areas that emphasized B & D) Coastline of the year 1992 (marked as the blue thin line) C & E) Coastline of the year 2020 (marked as the green thin line) 49 List of Tables Table Detailed information of the datasets used in this study 24 Table Land cover classes used in the study and its definition 25 Table List of environmental variables in the Philippines used for the three-model approach performed for this study and its percent contribution and Mean AUC values Model approach included all 19 bioclimatic variables with default regularization setting; model approach increased the regularization multiplier suggested after ENMeval calculations; Model approach includes the selected bioclimatic variables after autocorrelation 32 Table Percentage and size of area of each class for the classified image of the study area 45 Table Length of the Urban coastline from 1992-2020 48 Table Overall Accuracy and Kappa Coefficient of the classified datasets 48 List of Abbreviation AUC Area Under the Curve BARMM Bangsamoro Autonomous Region of Muslim Mindanao CALABARZON Cavite, Laguna, Batangas, Rizal, Quezon DTR Diurnal Temperature Range FT Feature Type GCM Global Climate Model IUCN International Union for Conservation of Nature LPPCHEA Las Piñas – Parañaque Critical Habitat and Ecotourism Area LULC Land Use Land Cover MIMAROPA Mindoro, Marinduque, Romblon, Palawan OLI Operational Land Imager RM Regularization Multiplier ROC Receiver Operating Characteristic TM Thematic Mapper USGS United States Geological Survey