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Master Thesis Tesis de MaestrĂ­a Submitted within the UNIGIS MSc programme at

Interfaculty Department of Geoinformatics- Z_GIS University of Salzburg Habitat Suitability Index for American Bullfrogs (Lithobates catesbeianus) in the United States of America By NicolĂĄs Francisco Soria Zurita 01322639 A thesis submitted in partial fulfilment of the requirements of the degree of Master of Science (Geographical Information Science & Systems) Advisors Supervisors:

Anton Eitzinger PhD Quito-Ecuador, October 24, 2019

MSc (GIS)

Science Agreement By presenting this signed and dated document, I solemnly swear that this thesis is completely my own work and not the work of anyone else. I have cited all the sources that I used in my thesis and have stated their origins in the References section of this document.

Corvallis OR- USA, October 24th /2019 (Place, Date)

(Signature)

Abstract American bullfrog (Lithobates catesbeianus) is one of the most hostile invasive species in the world. Their voracious eating habits and the lack of natural predators has increased the dispersal ability of the species.

Applying standard methods in Geographic Information Systems (GIS) with environmental data can support experts in monitoring, controlling, and mitigating the adverse effects of such invasive species. In this work, a Habitat Suitability Index (HSI) model to identify suitable habitats for potential distributions American bullfrog in the United States of America. HSI models use environmental data of the native of the species under study to identify new habitats where environmental conditions are similar that can support the settlement of new populations of the invasive species. The HSI model developed in this work employs Suitability Indexes (SI) models for three environmental variables terrain elevation, temperature data for the year 2014, and the land cover type of the United States of America. The outcomes of the HSI model using GIS tools identified areas where optimal environmental factors can support the establishments of the American bullfrogs.

Is imperative to develop and implement species distributions models to control and manage invasive species such as the American bullfrog. Such tools have the potential to support experts and environmental agencies to execute mitigation alternatives to protect native species and habitats. It is critical to build and implement species distributions models to control and manage invasive species such as the American bullfrog. The presented work provides some preliminary results for the implementation of the Habitat Suitability Index (HSI) with GIS to model possible distributions of the invasive American bullfrog in the United States.

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Resumen La Rana Toro (Lithobates catesbeianus) es una de las especies invasoras más agresivas en el mundo. Su apetito veros y la falta de predadores naturales han generado que la especie se disperse fácilmente. La implementación de Sistemas de Información Geográfica (SIG) y datos ambientales pueden facilitar el trabajo de expertos para el control, monitoreo, e implementación de soluciones contra los efectos negativos causados por especies invasoras.

El presente trabajo ha creado un modelo de Índices de Hábitat Disponible para identificar posibles hábitats que puedan albergar distribuciones de la Rana Toro en los Estados Unidos. El Modelo de Índices de Hábitat Disponible, o HSI por sus siglas en inglés (Habitat Suitable Index), utiliza datos climáticos del hábitat nativo de la especie invasora para identificar posibles nuevas áreas que presenten condiciones ambientales similares a la distribución nativa de la especie. El modelo HSI fue creado utilizando Índices de Disponibilidad, o SI por sus siglas en inglés (Suitable Indexes), para tres variables ambientales elevación del terreno, temperatura para el año 2014, y tipo de terrenos de los Estados Unidos. Los resultados preliminares de la aplicación de modelos HSI junto a herramientas GIS para el identificar posibles áreas que permiten el asentamiento de poblaciones de la especie invasora Rana Toro en los Estados Unidos.

Resulta de suma importancia desarrollar e implementar modelos de distribución de especies invasoras como la Rana Toro, los cuales pueden permitir a expertos y agencias ambientales ejecutar proyectos de mitigación y conservación de especies y hábitats nativos. El presente trabajo presenta resultados preliminares para la aplicación de modelos HSI junto a GIS para el identificar posibles distribuciones de la especie invasora Rana Toro en los Estados Unidos.

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Acknowledgment I want to express my gratitude and appreciation to my advisor MSc. Laure Collet. Her continued support has made it possible to expand my knowledge and expertise. She challenged and developed my critical thinking while guiding me on the development of this project.

I want to acknowledge Laure Collet and Gabriela Ramon. Thanks to your patience and support, I was capable of finishing this work. It has been a hard journey, but in the end, completing this thesis have fulfilled me as a professional. I would also like to thank my friend and academic advisor Fernanda Bonilla at UNIGIS for her persisting enthusiasm and motivation during my studies under the UNIGIS program. I want to thank all the professors at Oregon State University, the Blaustein Lab (Integrative Biology Department), and the Garcias Lab (Fisheries and Wildlife Department). Without their help, support, and motivation, this project would not have been completed. I want to give special recognition to Dr. Jenny Urbina. Jenny provided me guidance and advice during the development of the presented project, and we created a unique friendship. Jenny inspired many of the thoughts and ideas that have defined my research while providing constructive criticism and critical insights.

Last but not least, I would like to thank my parents, Diego y Kathya; to my sister Nia and the rest of my family. Your support and guidance have been genuinely immeasurable, and I appreciate all that you have done for me.

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Table of Contents 1.

INTRODUCTION & MOTIVATION ................................................................................ 9 1.1. RESEARCH QUESTION AND EXTENSION ................................................................................ 11 1.2. OBJECTIVE........................................................................................................................................... 12

2.

LITERATURE REVIEW ................................................................................................... 14 2.1. SPECIES ENVIRONMENTAL MATCHING MODELS (SEM) ............................................. 15 2.2. SPECIES DISTRIBUTION MODELS (SDMS) ......................................................................... 15 2.2.1. BIOCLIM ................................................................................................................................. 18 2.2.2. ECOLOGICAL NICHE BASE MODELING ...................................................................... 19 2.2.3. MAXENT .................................................................................................................................... 20 2.2.4. HABITAT SUITABILITY INDEX (HSI) ............................................................................. 22

3.

METHODOLOGY ................................................................................................................ 25 3.1. STUDY AREA ...................................................................................................................................... 26 3.2. ENVIRONMENTAL VARIABLES .................................................................................................... 27 3.3. DATA PREPARATION ....................................................................................................................... 29 3.4. BUILDING THE SUITABILITY INDEX VALUES USING GIS ................................................ 32 3.4.1. SUITABILITY INDEX VALUE FOR TERRAIN ELEVATION USING GIS ................ 32 3.4.2. SUITABILITY INDEX VALUE FOR TEMPERATURE USING GIS ........................... 33 3.4.3. SUITABILITY INDEX VALUE FOR THE LAND COVER TYPE USING GIS .......... 34 3.5. BUILDING THE HABITAT SUITABILITY INDEX ....................................................................... 35 3.6. ANIMATION FOR THE HABITAT SUITABILITY INDEX.......................................................... 35

4.

RESULTS & DISCUSSION............................................................................................ 37 4.1. SUITABILITY INDEXES ..................................................................................................................... 37 4.1.1. SUITABILITY INDEX (HSI) FOR TERRAIN ELEVATION .......................................... 37 4.1.2. SUITABILITY INDEX (HSI) FOR TEMPERATURE ...................................................... 38 4.1.3. SUITABILITY INDEX (HSI) FOR THE LAND COVER TYPE .................................... 52 4.2. HABITAT SUITABILITY INDEX ....................................................................................................... 54 4.3. GIF ANIMATION................................................................................................................................. 67 4.4. DISCUSSION ........................................................................................................................................ 67

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CONCLUSIONS & RECOMMENDATIONS .......................................................... 73

REFERENCES: ............................................................................................................................. 75 APPENDIX ....................................................................................................................................... 82 A. ANIMATION CODE............................................................................................................................. 82

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Table of Figures Figure 1 AUC MaxEnt example (Phillips et al., 2006)...................................... 21 Figure 2 Flow chart Habit Suitability Index for American bullfrog in the United States of America. ..................................................................................... 26 Figure 3 Area of Study United States of America ......................................... 27 Figure 4 Suitability Index for American bullfrogs according to elevation......... 30 Figure 5 Suitability Index for American bullfrogs according to temperature ... 31 Figure 6 Suitability Index for American bullfrogs according to cover type ...... 32 Figure 7 Presence of American bullfrog in the United States & Suitability Index for terrain elevation .................................................................................... 38 Figure 8 Suitability Index for temperature - January 2014 .............................. 40 Figure 9 Suitability Index for temperature - February 2014 ............................. 41 Figure 10 Suitability Index for temperature - March 2014 ............................... 42 Figure 11 Suitability Index for temperature - April 2014 .................................. 43 Figure 12 Suitability Index for temperature - May 2014................................... 44 Figure 13 Suitability Index for temperature - June 2014.................................. 45 Figure 14 Suitability Index for temperature - July 2014 ................................... 46 Figure 15 Suitability Index for temperature - August 2014 .............................. 47 Figure 16 Suitability Index for temperature - September 2014 ....................... 48 Figure 17 Suitability Index for temperature - October 2014 ............................ 49 Figure 18 Suitability Index for temperature - November 2014......................... 50 Figure 19 Temperature Suitability Index - December 2014 ............................. 51 Figure 20 Suitability Index for land cover type ................................................. 53 Figure 21 Habitat Suitability Index January 2014 ............................................ 55 Figure 22 Habitat Suitability Index February 2014........................................... 56 Figure 23 Habitat Suitability Index March 2014 ............................................... 57 Figure 24 Habitat Suitability Index April 2014 .................................................. 58 Figure 25 Habitat Suitability Index May 2014 .................................................. 59 Figure 26 Habitat Suitability Index June 2014 ................................................. 60 Figure 27 Habitat Suitability Index July 2104 ................................................... 61 Figure 28 Habitat Suitability Index August 2014 .............................................. 62 Figure 29 Habitat Suitability Index September 2014 ....................................... 63 Figure 30 Habitat Suitability Index October 2014 ............................................ 64 Figure 31 Habitat Suitability Index November 2014 ........................................ 65 Figure 32 Habitat Suitability Index December.................................................. 66

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Table of Tables Table 1 Possible uses of Species Distribution Models (SDM) ........................ 16 Table 2 Suitability Index Value ......................................................................... 23 Table 3 Suitability Index for Temperature January .......................................... 34 Table 4 Layer code for temperature Suitability Indexes .................................. 34 Table 5 Land Cover Type SI............................................................................. 35

List of Acronyms AUC - Area Under the Curve Bd - Batrachochytrium dendrobatidis DEM - Digital Elevation Model GARP - Genetic Algorithm for Rule-set prediction GBIF - Global Biodiversity Information Facility GIF - Graphical Interchange Format GIS - Geographic Information Systems GPS - Global Positioning Systems HSI - Habitat Suitability Index IUCN - International Union for the Conservation of Nature LIDAR - Light Detection and Ranging NLCD - National Land Cover Dataset OSU - Oregon State University PNW - Pacific Northwest SDMs - Species Distribution Models SEM - Species Environmental Matching models SI - Suitability Index TIFF - Tagged Image File Format USGS - United States Geological Survey

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1. Introduction & Motivation The loss of biodiversity since the industrial age is a primary international concern (Pimm, Russell, Gittleman, & Brooks, 1995). Although scientists cannot give an exact number of species lost, it is estimated that the current rate of extinction is more significant than any known in the last 100,000 years (Eldredge, 1998). Despite scientists interest in understanding these losses, an ecological theory has been able to provide little predictive insight into these problems. The complexity of the process behind the problem is recognized, and research programs directed to understand species losses are usually focused on the direct effects of single factors. One of the significant declines in biodiversity is being reported in amphibian species (frogs, toads, salamanders, and caecilians) (Blaustein & Kiesecker, 2002). However, recent studies about this particular problem suggest that global amphibian losses are the result of interactions between different factors (Blaustein & Wake, 1995).

The loss of the amphibian population worldwide was first recognized in 1989 as a phenomenon that deserved world attention (Blaustein & Wake, 1990). In 1993 during the World Herpetological Conference, it was reported by scientists than 500 populations of frogs and salamanders was in decline in different world locations (Alford, 1999). Scientific concern about the decline of amphibians populations is in large part due to their value as indicators of environmental stress and their vital role within both aquatic and terrestrial communities (Blaustein, 1994; Blaustein & Wake, 1995; Semlitsch & Bodie, 2003). However, multiple reasons are supporting why we need to be alarmed about amphibian population decline. Amphibians are widely used model organisms for the study of cellular and developmental biology as well as immunology, genetics, and genomics (Klein et al., 2002) and represent a source of valuable medical-pharmaceutical agents (Clark, 1997). Compounds from amphibian skin have anti-inflammatory and analgesic properties.

Amphibian skin contains a variety of antibacterial, antifungal, and antiviral substances that are effective in treating human diseases like cancer (Lu, Nan, & Lei, 2008) and pathogens like the human immunodeficiency virus (HIV) (VanCompernolle et al., 2005). Several causes are implicated in the decline of amphibians worldwide habitat destruction and alteration, pollution, diseases, 9

global environmental change, overexploitation, and introduced species (Blaustein & Kiesecker, 2002). Introduced species are critical drivers of population decline and extinctions in different taxa (Bellard, Cassey, & Blackburn, 2016).

Amphibians are not the exception; invaders may compete, prey upon native amphibian species or introduce diseases to them. For example, Mosquitofish (Gambusia affinis) and Crayfish (Procambarus clarkii) were brought to California by humans and are active predators of larval newts (Gamradt & Kats, 1996). Hatchery-reared salmonid fishes may eat native amphibians or infect them with pathogens (Kiesecker, Blaustein, & Miller, 2001). The American bullfrog (Lithobates catesbeianus) considered one of the 100 worst invaders by the International Union for the Conservation of Nature (IUCN) can outcompete, eat and transmit diseases to native amphibians (Garner et al., 2006; Kats & Ferrer, 2003; Schloegel et al., 2010). The native range of American bullfrog covers Eastern North America, from the Southern United States to Southern Canada but the species has been introduced globally for different purposes since the 1800s (Ficetola, Thuiller, & Miaud, 2007). Once established, populations of American bullfrog are either difficult or impossible to eradicate (Adams & Pearl, 2007) and can have a significant negative impact on native amphibians through competition, predation (Kats & Ferrer, 2003) and disease transmission. American bullfrog can be healthy carriers of the fungus Batrachochytrium dendrobatidis (Bd) (Garner et al., 2006; Schloegel et al., 2010). This pathogen causes chytridiomycosis, a new infectious disease implicated in the decline of amphibian populations on a global scale (Berger et al., 1998; Lips et al., 2006; Pounds et al., 2006). The expansion of the American bullfrog is a problem for native species. It is necessary to take preventive measures to try to control and mitigate the adverse effects of the expansion.

However, first is essential to understand the patterns behind the rapid and effective extension of this particular invasive species. The question of a conservative biologist is, how and why the American bullfrog is expanding so fast in the Pacific Northwest of the United States of America? To find an answer to this question, it is necessary to understand the species distribution. It is also essential to understand what environmental variables control the behavior of the species. If conservative biologist wants an answer to all these questions, 10

they need to construct a distribution model that can be capable of simulating the environmental conditions that allow the distribution of the bullfrog as an invasive species. Thanks to the fast development of computer sciences and Geographic Information Systems (GIS), it is possible to create models that combine geographical information with environmental variables. These tools can be used to simulate suitable conditions that can increase the population and expansion of a particular species. The results of the simulation can be used as layers in maps to identify areas that have higher probabilities to host the invasive species. American bullfrog in North America is the focus of the present study. This work shows how the Habitat Suitability Index Model (HSI) can be used as a baseline to model the potential expansion of this invasive species.

The HSI model is built using GIS methodology that relates the distribution of the invasive species according to changes in its environment. This tool can be used to formulate mitigation strategies aiming to control the increasing expansion of American bullfrogs and mitigate the adverse effects in native populations of diverse species.

1.1.

Research question and extension

The expansion of the American bullfrog in the Pacific Northwest represents a threat to local species. Local amphibian and other species of fishes, small mammals, insects, and birds are threatened by this invasive species (Snow & Witmer, 2010). At Oregon State University (OSU) (Corvallis, OR), the Blaustein Lab (Integrative Biology Department), and the Garcias Lab (Fisheries and Wildlife Department) have a particular interest in the American bullfrog. Their research covers several aspects of the biology and ecology of the species. Among the topics of their research, they want to understand the impacts of American bullfrog in local habitats, native species, and its interaction with environmental variables.

It is hypothesized that as global environmental change modifies habitats in the Pacific Northwest, the American bullfrog is expanding. As a result, the populations of native amphibians are decreasing. Therefore, the identification of potential areas of expansion for the invasive species will allow scientists and authorities to implement mitigation procedures, preventing adverse effects of

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intrusive development. Scientists could use models to predict possible expansion scenarios with current and projected environmental factors.

1.2.

Objective

The purpose of this work is to use a HSI model as a mechanism to identify the possible expansion of the American bullfrog in the Pacific Northwest (PNW) of the United States. To develop the HSI model, first is necessary to identify the climate parameters such as temperature and precipitation data to determine a Suitability Index (SI) for the American bullfrog.

By combining the SI and environmental data for the PNW of the United States, an HSI model can be generated to simulate possible expansion scenarios showing areas where the American bullfrog can overrun local species in the PNW. In this work, the construction of the HSI model for the American bullfrog in the United States of America is illustrated step by step.

Future work needs to be completed to validate the accuracy of the HSI model.

It is necessary to compare the results of the simulation with the information provided by the researchers associated to the Integrative Biology and the Fisheries and Wildlife Departments at OSU, in Corvallis, Oregon with other sources of information as data obtained from biodiversity databases (Global Biodiversity Information Facility - GBIF).

This thesis document is organized as follows. First, the Literature Review chapter provides relevant information about the current state of the art regarding modeling species distribution using GIS. The Methodology chapter introduces the variables and procedures required for the construction of the HSI model. This chapter describes the development of the SI for each variable that affects the distribution of the American bullfrog

followed by the

implementation of GIS tools within ArcGIS to build the final HSI model simulation.

The Results & Discussion chapter begins presenting the results for the SIs for three environmental variables. Next, the resulting HSI model for the American bullfrog in the United States for the twelve months of the year 2014 are illustrated. The Discussion section explores the takeaways from the implementation of HSI as a modeling tool. Next, an analysis of data an

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interpretation of the findings regarding the environmental variables used, the SIs, and the implementation of the HSI model is presented.

Finally, the Conclusion and Recommendation chapter summarizes the initial results of the project and examines the avenues for future work within the implementation, validation, and optimization of the HSI model for the American Bullfrog.

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2. Literature Review Global Positioning Systems (GPS) and GIS revolutionize the analysis, representation, and use of spatial data. Individuals using GIS tools can elaborate maps and realized complex analyses that otherwise would be difficult to construct. The individuals can use satellites networks, mapping software, and databases to perform data analysis and data administration, which makes the process of view and manipulate spatial information simpler (Holcombe, Stohlgren y Jarnevich, 2007).

GIS has been implemented in different areas such as agriculture, industry, health-care, urban-planning, and ecology, among others. Fields such as ecology and biology need to consider for any analysis, not only spatial data but also the interaction between environmental data with spatial data. Location is one of the most important variables used to understand the behavior of an animal (Austin, 2002). Combining that information with weather data can be used to understand the migration or feeding process.

Following this potential advantage, GIS can be used to explain expanding distributions of invasive species. This information can be used and managed for early detection and rapid response. For instance, one of the fundamental requirements for a successful invasion is the resemblance of the climate between native and target regions (Ficetola et al., 2007). To model the climatic niche, scientists gather data on the native range of the species. The data can be used to locate areas where the native species characteristics are equivalent. By using GIS tools and data of climate and land use, different modeling techniques have been developed to study and evaluate the risk of an invasion (Thuiller et al., 2005). Modeling species distribution and their suitable areas are vital activities nowadays not only for primary biology studies but also for conservation studies and management actions (Stockwell & Peterson, 2002).

The following subsections cover a literature review on state of the art about different species distribution models and how they differ from each other.

First is presented what a Species Environmental Matching models (SEM) are,

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followed by the introduction of Species Distribution Models (SDMs). Finally, the HSI model is presented.

2.1.

Species Environmental Matching Models (SEM)

The environment largely determines species distributions. Statistical models called SEM are used to determinate current and potential species distributions. SME models relate environmental factors to observed species distributions (Holcombe et al., 2007). Using environmental relationships, the model projects the spatial species movements in reaction to environmental changes.

SEM models relate detected species distribution to environmental factors such as temperature, precipitation, topographic, and edaphic. Then, assuming the stable relationships between the environmental factors, the model project species spatial changes in response to the spatial variation of environmental factors under the current conditions. The environmental factors are used to build a gradient that arranges proximal to distal predictors, which may have a direct or indirect effect on the species distribution (Austin, 2002). Most of SEM models are created with the use of GIS because it allows a visual representation of the environmental envelope and potential habitat or abundance for different environmental factors. Nevertheless, this model has limitations with relative recent invasive species. Recent invaders may not necessarily have filled all suitable habitats, so the model won t be accurate because it is using inappropriate predictors. Defining where a species distribution may settle down depends on identifying an existing or potential habitat with the necessary environmental conditions. GIS technology provides an avenue to produce these types of analysis more accessible to the general public. Data of environmental and ecological factors is now available for free on the Internet, simplifying the construction of the SEM of models.

2.2.

Species Distribution Models (SDMs)

SDMs are used to predict ecologically suitable areas for the establishment of invasive species using climate projections and species distributions (Nori, Urbina Cardona, Loyola, Lescano, & Leynaud, 2011). SDMs models describe the Grinnellian niche of the organism (Soberรณn, 2007), 15

allowing the identification and comparison of geographical areas with similar environmental conditions. A Grinnellian niche can be defined by scenopoetic variables and environmental conditions on large scales that are important to understanding geographic and environmental properties of species (James, Johnston, Wamer, Niemi, & Boecklen, 1984). Scenopoetic variables describe environmental variables that do not interact with other variables and change very slowly (Austin & Smith, 1989).

The use of SDMs has increased dramatically in recent years because of the full range of applications. That includes studies in ecology and conservation biology. The information and data gathered are used as predictors as the user can extrapolate or interpolate the data from different environments (Elith & Leathwick, 2009). This kind of approach leads to evaluate hypotheses about the contribution of various factors in the current distribution of species. Some possible uses of SDM in ecology and conservation biology applications are listed in Table 1 (Guisan & Thuiller, 2005). Different researchers have accomplished different objectives with the use of SDM. The table summarizes some of the most relevant application of SDM in the past twenty-five years. As part of the development of this work, these studies were reviewed and explored.

Table 1 Possible uses of SDM (Guisan & Thuiller, 2005)

Type of use & application Quantifying the environmental niche of species Testing bio-geographical, ecological and evolutionary hypotheses Assessing species invasion and proliferation Assessing the impact of climate, land use and other environmental changes on species distributions Suggesting surveyed sites of high potential of occurrence for rare species Supporting appropriate management plans for species recovery and mapping suitable sites for species reintroduction Supporting conservation planning and reserve selection Modeling species assemblages from individual species predictions

References Austin et al. (1990), Vetaas (2002) Leathwick (1998), Anderson et al. (2002), Graham et al. (2004b) Beerling et al. (1995), Peterson (2003) Thomas et al. (2004), Thuiller (2004) Elith & Burgman (2002), Raxworthy et al. (2003), Engler et al. (2004) Pearce & Lindenmayer (1998) Ferrier (2002), AraĂşjo et al. (2004) Leathwick et al. (1996), Guisan & Theurillat (2000), Ferrier et al. (2002)

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SDMs relate environmental variables with field observations of the species based on statistically derived response surfaces (Guisan & Zimmermann, 2000). Species data is collected and estimated by field sampling. Observation and sampling methods record the presence, abundance, and absence of species. Environmental predictors can have direct or indirect effects on species distributions. The predictors can reflect three primary types of influences on the species (Guisan & Thuiller, 2005): Limiting factors: controlling species eco-physiology (water, soil composition, temperature, etc.).

Disturbances: perturbations are affecting environmental systems.

Humans or natural phenomena can cause disruptions.

Resources: all compounds that can be used by the species (energy, water).

Environment data associated with these three types of influence on species distribution can be deployed with GIS. For example, a continuous species distribution located over a large area and at crude resolution is likely to be ruled by climate factors. While irregular distribution situated over a smaller area and at fine resolution is more likely to result from uneven distribution of resources, driven by micro-topographic variation or habitat fragmentation (Scott, Heglund, & Morrison, 2002).

The SDM model construction follows six phases: conceptualization, data model, model fitting, model evaluation, spatial predictions, and model assessment. During the conceptualization phase, possible problems of studying the selected organism are considered. For example, the approach used to detect a sessile species would be different from mobile species. The challenge of such a task will increase significantly if the species has a low detectability rate. That is the reason why it is necessary to consider direct or indirect predictors and to select the best strategy for sampling the species.

The data model phase starts gathering the species data. It is necessary to complete a cartographic and statistical exploration. Additionally, during this phase is essential to define the scale of the sampling.

The model-fitting phase requires justifying and validating the type of data and metadata used in the study (Soberรณn, 2007).

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The model evaluation phase is the most critical step during the SDM model construction. During this phase, the available data and the significance and influence of environmental predictors for the species under study are evaluated (Thuiller et al., 2005). Likewise, in this phase, members of the team define the sampling strategy for the collection of new data and determine the geographical scale required for the model.

During the spatial predictions phase, the hypothesis and objectives of the study are defined. Furthermore, the data and methodology to be used are selected (Austin, 2002).

Finally, during the model assessment phase, the users verify the data sampling strategy, review the environmental factors, and select the geographical scale for the final model. However, in the current SDM model construction practice, few decisions are made during the initial phase of the study (Guisan & Thuiller, 2005). The lack of information about the species, the geographical area, and the associated environmental data will limit the accuracy of the final model. During each phase of the SDM model construction, all the variables and data need to be carefully checked to distinguish potential problems (Guisan & Zimmermann, 2000).

GIS provide tools for storing and manipulating both species and environmental data. SDMs evolved when the new statistical methods were linked with GIS-based environmental layers (Elith & Leathwick, 2009). SDMs work alongside GIS to develop models and perform the analysis of species and environmental factors.

Since the year 2010, an impressive diversity of tools and methods have been developed for constructing SDM. Each technique differs on how to use the predictors and ecological variables for modeling the species distribution.

The following subsections detail some of the most popularly used SDM: BIOCLIM, Ecological Niche Based Modeling, MaxEnt, and HSI.

2.2.1. BIOCLIM Bioclimatic Prediction and Modeling System also known as BIOCLIM is the first SDM generated to use individual species data and climate interpolation estimates for creating a map based on spatially coarse climate data (Busby, 1991). BIOCLIM can be used for identifying geographical locations where a 18

particular species may reside using known data of site and climate for the species (Beaumont, Hughes, & Poulsen, 2005). The potential distribution of a species is identified using Digital Elevation Model (DEM) and climate data for each terrain elevation layer. The data is interpolated and compared with the observed species profile climate. BIOCLIM then identify geographical locations where the species profile match with the resulting interpolation. BIOCLIM can interpolate up to 35 climatic parameters for any site where the latitude, longitude, and elevation are known (Busby, 1991).

A disadvantage of using BIOCLIM is the difficulty to generate accurate models when species are not well associated with climate. Additionally, it is a wrong assumption to consider climate data as the only factor playing an essential role in determining species distributions (Booth, Nix, Busby, & Hutchinson, 2014). Other environmental factors as the land-cover type and soil type have a strong influence in the distribution of species.

2.2.2. Ecological Niche Base Modeling A species niche is defined as the group of environmental conditions necessary for a species to keep a stable population through time (Chase & Leibold, 2003). Employing GIS tools allow the construction of species distribution models based on environmental factors correlated with the ecological niche of a species (Guisan & Thuiller, 2005). Species distribution models using GIS can successfully predict geographical distributions of different species identifying geographic locations where the species have a high probability of presence without the need for sampling (Peterson, 1999).

One problem with the GIS model approach is the lack of data regarding distinct species interactions or historical factors (AraĂşjo & Guisan, 2006).

GIS niche-based models that focus on environmental factors with closely related species in adjacent or overlapping areas can be used to study how distributions of species are influenced by environmental factors and competitive roles (Costa, Wolfe, Shepard, Caldwell, & Vitt, 2008). GIS niche-based model versatility allows scientists to distinguish and identify species that could be a severe threat as invaders in habitats where climate and environmental conditions are a match (Thuiller et al., 2005).

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Giovanelli, Haddad & Alexandrino (2007) presented an ecological niche modeling using Maxent to predict the potential distribution of American bullfrog in Brazil. The following subsection explains with more detail how MaxEnt models work. Similar work completed by Nori et al. (2011) and Urbina Cardona, Nori, & Castro (2011) modeled the expansion of the American bullfrog in Argentina (Nori et al., 2011) and in Colombia (Urbina-Cardona et al., 2011).

Some of the examples of the capabilities of the Ecological Niche Base Model are presented in the work of Costa et al. (2008), and Stockwell & Peters (1999). Costa et al. (2008) investigated potential factors that could limit distributions of closely related species using a niche-based model with a genetic algorithm for rule-set prediction (GARP) (Costa et al., 2008). GARP algorithms use an artificial intelligence approach based on location data points to generate predicted distributions of different species using environmental factors (Stockwell & Peters, 1999).

2.2.3. MaxEnt MaxEnt is a modeling technique that uses ecological factors influencing species distribution and creates species distribution maps by incorporating point locality records for species presence and environmental variables (features) that can be continuous or categorical. In general, 70% of the observations for species are selected as training data, and the remaining 30% of the data points are used to test the model output (Phillips, Anderson, & Schapire, 2006). MaxEnt requires as input data the species geographical position and environmental predictors (precipitation, temperature, etc.) across grid cells that represent the landscape defined by the user. From the defined landscape, MaxEnt contrasts the species known occupation sites against background locations where the presence of the species is unknown (Merow, Smith & Silander, 2013). MaxEnt model expresses a probability distribution where each grid cell has a suitability value (predicted) of conditions for the species under study. Under appropriate assumptions about the input data and biological sampling records, the output can be interpreted as the predicted probability of presence or as predicted local abundance. A high value of the function at a particular grid cell indicates that the grid cell is predicted to have suitable conditions for the presence of that specific species.

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MaxEnt outputs include Area Under the Curve (AUC) (Figure 1), the Jack-knife variable of importance, and a distribution map of the species. The Jack-knife variable of concern is the variable explaining most of the information in the model even if the variable is isolated. Similarly, if the variable is omitted from the Jack-knife test, the gain of the model decreases considerably (Steven, 2006).

The AUC provides a measure of how well the model predictions discriminate between locations where observations are present or absent. The value goes between 0 and 1. If the value is 0.5, the model performance is not different from that obtained by random while a score close to 1 could be considered a good model. A value of 0.7 means there is a probability that 70% of the times that a random selection is made from the presence records will have a score higher than a random selection from the absence records (Phillips et al., 2006).

Figure 1 AUC MaxEnt example (Phillips et al., 2006).

In the case of American bullfrog, work completed by Giovanelli et al.

(2007) in Brazil demonstrate that it is possible to model the distribution of invasive species using MaxEnt to model the ecological niche. They produced the model for the American bullfrog using 784 geo-referenced occurrence points in North America, nineteen bioclimatic layers and one topographic layer as predictors. The resulting model was projected into North America and Brazil 21

to compare the potential geographic distribution of the American bullfrog modeled with MaxEnt with the actual location points of species occurrence (Giovanelli et al., 2007).

Ficetola et al. (2007) presented a model using MaxEnt to predict the potential distribution of American Bullfrog in Europe. Ficetola et al. (2007) observed a relationship between the probability of occurrence (presence) for the invasive amphibian and three variables precipitation, temperature, and human footprint. Areas with high rainfall during the whole year, average minimum annual temperature, and human presence were areas with higher suitability values for the American bullfrog in Europe (Ficetola et al., 2007).

2.2.4. Habitat Suitability Index (HSI) HSI is a type of niche model used to distinguish suitable habitats for a particular species and identify their presence in the habitat under study. HSI models are used for rapid assessments such as environmental impact and have many applications (Vinagre, Fonseca, Cabral, & Costa, 2006). Combining GIS methodologies with HSI models generate species distribution maps, which can be used by environmental organizations to make informed decisions (Bovee & Zuboy, 1988; Terrell, 1984). GIS can be used to efficiently connect and model spatial and temporal data (variables) to identify suitable habitats for a particular species. Management applications can include events such as: evaluating impact scenarios or regulatory alternatives; identifying and prioritizing areas for conservation actions; predicting impacts of environmental change (Brown et al., 2000). The HSI models should be viewed as hypotheses of species-habitat relationships rather than statements of proven cause and effect relationships. Their value is to serve as a basis for improved decision making and increased understanding of habitat relationships because they specify hypotheses of habitat relationships that can be tested and refined.

HSI models for a species are built combining different SIs of the species under study. SIs reveal habitat quality as a function of environmental factors (Terrell, 1984). One problem when developing species models is selecting which variables need to be considered in the model. Ideally, management species models should use a small number of essential variables keeping the model simple. However, species dynamics are complex and dependent on 22

multiple and combined factors (Vinagre et al., 2006). SI assigns numerical values between zero and one to each specific environmental variable. The value change depending on how likely the range is for the presence or occurrence of the species under study. Table 2 shows the SI assigned values according to the favorable or unfavorable conditions. A SI with a value of one is applied to habitats where the species have a high density or actual occurrence. Contrary, SI values of zero are used in habitats where the species have no presence. The SI is usually applied to describe species correlation with environmental and geographical variables such as temperature, terrain elevation, radiation, precipitation, type of vegetation, among some.

Table 2 Suitability Index Value

Suitability Index Value 1 0.5 0.1 0

Description of habitat use High density active occurrence Common occurrence or average density in field Low density - rare occurrence Little or no occurrence

HSI models use mathematical terms for calculating the SI of habitat as a function of more than one environmental variable. The model is constructed based on SIs that replicates habitat quality over different possible ecological conditions for the invasive amphibian species. The SIs for each defining variables are combined to create an HSI data model. The HSI data can be used with GIS tools to develop and analyze maps with information about potential habitat locations for the invasive species under analysis.

The literature review completed in this thesis examined different HSI models used to map different species habitat quality. In 2012 Cho, Lee, Hong, Kim, & Kim applied HSI model to find suitable site selection for oyster farms in Geoje-Hansan Bay in Korea (Cho et al., 2012). Their results showed a high correlation between HSI and the productivity of local oyster farms. In 2006, Vinagre used HSI models to map habitat quality for the juveniles soles, Solea solea, and Solea senegalensis in the Tagus estuary in Portugal (Vinagre et al., 2006). The sole, or black sole is a species of flatfish in the family Soleidae.

During 1982 to 1987 Allen created HSI models in the United States of America for different mammals species including beavers, fishers, pronghorn, moose, 23

swamp rabbit, and fox squirrel (Allen, 1982, 1983, 1985, 1987; Allen, Cook, & Armbruster, 1984; Allen, Energy, & Team, 1983). In 1987, Graves and Anderson developed a HSI model for the bullfrog (Rana catesbeiana). Graves model consolidated habitat use information to derive quantitative relationships between key environmental variables and habitat suitability (Graves & Anderson, 1987). In this work, we are going to applied HSI model with three environmental variables (terrain elevation, temperature, and land cover type) to determine the geographical distribution of the American bullfrog in the United States of America.

24

3. Methodology In this chapter the methodology used to build the HSI Model for the American bullfrog in the United States of America is described. Observations of American bullfrogs have already been reported in different areas across the PNW of the United States of America. Environmental agencies at PNW need rapid assessment tools to focalized efforts towards mitigating the negative impact of the American bullfrog on native species and habits. The HSI has the advantage of creating accurate models faster than any of the other species distribution and environmental models covered in the literature review.

Additionally, HSI models define the hypothesis of species-habitat relationships that can be evaluated with field observations. For this study, HSI models can map quantitative relationships between key environmental variables and habitat suitability for the American bullfrog in PNW. The following subsections describe in detail the procedure used to develop the HSI Model for the American bullfrog in the United States of America. All the tables, diagrams, graphs, and maps presented in this document were created as part of this work by the author.

The flowchart used to build the HSI for the American bullfrog in this work is described Figure 2. The first step is defining each environmental variable.

For this study, three variables were selected terrain elevation (DEM), temperature, and land cover type (extracted from the National Land Cover Dataset - NLCD). The variables were chosen because they are a direct influence parameter to the distribution range of the American Bullfrog. The second step is the generation of the SI for each environmental variable. Finally, the third step is to calculate the HSI for the American bullfrog by multiplying each SI.

25

Figure 2 Flow chart Habit Suitability Index for American bullfrog in the United States of America.

3.1.

Study Area

The American bullfrog is originally from the east of the United States.

The American bullfrog has expanded across the entire country and is considered a big threat to native amphibian s species on the west of the United States (Ficetola et al., 2006; Loyola, Nori, & Nicola, 2011).

The objective of this work is to build a HSI model as a tool to distinguish possible areas of expansion of the American bullfrog in the PNW of the United States. To generate an accurate model is fundamental to include a large set of data points. However, the available observation data points for the American bullfrog for the PNW is limited when compared to the rest of the observation data points of the entire country. It was decided to use the available data of the whole United States of America to enhance the accuracy of the HSI model.

Figure 3 presents the area of study used in this work. The states of Alaska, Hawaii, and the unincorporated US territory of Puerto Rico were

26

excluded from this analysis. The map defines the study area for the HSI model in this project.

6/25/2019

ArcGIS - United States of America

United States of America

United States of America USGS The National Map: National Boundaries Dataset, 3DEP Elevation Program, Geographic Names Information System, National Hydrography Dataset, National Land Cover Database, National Structures Dataset, and National Transportation Dataset; USGS Global Ecosystems; U.S. Census Bureau TIGER/Line data; USFS Road Data; Natural Earth Data; U.S. Department of State Humanitarian Information Unit; and NOAA National Centers for Environmental Information, U.S. Coastal Relief Model. Data refreshed October 2018.

Figure 3 Area of Study – United States of America

3.2.

Environmental Variables

This section describes the three variables used in this project terrain elevation, temperature, and the land cover type, followed by the different steps completed to adjust the data before building the HSI model. The HSI model created in this project uses information of the American bullfrog (Lithobates catesbeianus), collected by the Department of Integrative Biology from Oregon State University, in Corvallis, Oregon; and observation records reported in biological, geographical, and environmental databases.

The first step towards developing an HSI model is to distinguish the key https://www.arcgis.com/home/webmap/print.html

ecological requirements of the American bullfrog. For instance, the climate similarity between native and target habitats is considered an essential element for a successful establishment of an invasive species (Beebee & Griffiths, 2005; Welk, Schubert, & Hoffmann, 2002). Although the climate, in many cases, is the single most crucial factor, is not the only parameter that matters. Different biotic factors and distinct geographical aspects play an essential role in the successful development of an invasive species (Stohlgren & Schnase, 2006).

Terrain elevation is a crucial factor that controls the distribution of a species.

27

1/1

Terrains with high altitudes are considered an obstacle for species migration.

DEMs are digital representations of the surface created from terrain elevation data. DEMs can be represented as vector network or as a raster. DEMs are constructed using remote sensing techniques and land surveying. GIS tools can use data from DEMs to produce different types of models, maps, visualization applications, and other applications.

In this work, terrain elevation data (DEMs) in combination with climate (temperature) data collected from the United States are used to analyze the potential distribution of the American bullfrog. These parameters in the model can be used to recognize potential migration routes and explain the behavior of the invasive amphibian as temperature changes during an entire year.

As the average temperature in the U.S. increases, it is expected to identify new areas with climate matching for the invasive American bullfrog will be suitable (Bellard et al., 2013). The information generated by the HSI model can be used to identify sensitive areas that could be vulnerable to the current expansion of the American bullfrog, allowing authorities to take preventive measures.

Biological databases store distinct data information for a large number of species and habitats. For this study, biological databases were examined to identify locations records for observation and sampling of the American bullfrog.

Information from the GBIF, which comes mainly from museum records around the world, was used to find the terrain elevation range of the species as well as information about the habitat used by American bullfrogs. The data was filtered to extract data representing the confirmed observations of American bullfrogs in the United States. The original dataset collected 5374 confirmed observations. Records for Puerto Rico were excluded due to technical issues (i.e., no inclusion of datum), and data samples from Hawaii were also excluded due to the uncertainty of its coordinate system that results in an error up to 600 meters. Furthermore, a second filter process was performed to remove data without information of the horizontal datum. The final dataset contained 1997 data points representing confirmed observations of the American Bullfrog in the United States of America.

The States of the Pacific Northwest presented a small set of observations data points. The States of Oregon and Washington had only 16 and 102 28

confirmed observation points each. The absence of representative data points for the states of the Pacific Northwest would not allow building a useful and accurate model. If the model includes more data points, diversity on vegetation type, terrain elevation relationships, and temperature data, the final result would be a more precise model. Then, it was decided to build the HSI model for the entire United States of America.

3.3.

Data preparation

The first environmental variable to be considered in the HSI model is the terrain elevation. DEMs were extracted using the latitude and longitude information.

The

United

States

Geological

Survey

(USGS)

website

(eros.usgs.gov) provides Raster DEM data from the United States of America.

The terrain elevation data needs to be associated with information of the American bullfrog to create a SI for terrain elevation.

The American bullfrogs have been reported in habitats located at the lowest terrain elevation corresponding to 1.5 meters and a maximum terrain elevation of 1500 meters above the sea level (Snow & Witmer, 2010).

Therefore, the values assigned for the SI at terrain elevation between 1.5 to 1500 meters above the sea level have an SI value of 1 unit, indicating those elevations as suitable habitats for the American bullfrog. While terrain elevations below 1.5 meters and above 1500 meters have an SI value of 0, meaning those terrain elevations are not suitable habitats for the American bullfrog. The histogram presented in Figure 4 shows the Suitable Index of the American bullfrog related to terrain elevation. Suitable Index (SI) for terrain elevation in combination with the DEM of the United States allows the visualizing in a map of areas that can support distributions of the American bullfrog considering the terrain elevation SI.

29

Suitability Index

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Suitability index

0

1.5 15 150 Elevation (meters)

1500

Figure 4 Suitability Index for American bullfrogs according to elevation

The second environmental variable to be considered in the model is temperature. The temperature during the year changes seasonally, and this variation in temperature set different milestones in amphibian s life. In the case of American bullfrogs, male vocalization to attract females for mating coincides with a temperature of 27°C (Howard, 1978; Wright & Wright, 1933). Closer to winter, American bullfrogs begin hibernation when water temperatures are below 16°C (Yuell, Moyle, & Baskett, 1956). Emergence from hibernation in spring occurs when air temperatures range from 19°C to 24°C and water temperatures are around 13°C (Wright, 1914). The PRISM Climate Group provided the necessary temperature data to create the SIs for temperature.

PRISM Climate Group gathers climate information from a wide range of monitoring networks to develop spatial climate datasets. PRISM website http://www.prism.oregonstate.edu/ provides free access to data climate data of the United States. In this work, the SI for temperature was created using the climate data sets for the year 2014.

Considering the previous information (natural history of the American bullfrog), and the absence of specific data for water temperature, the model shows the Suitable Index (SI) for temperatures between 19°C to 27°C (Figure 5). Therefore, the values assigned for the SI at the temperatures between 19°C to 27°C will have a value of 1, indicating the temperature range, which is suitable for the American bullfrog. SI values of 0 were assigned to temperatures

30

lower than 19°C and temperatures higher than 27°C, indicating the

Suitability Index

physiological constraints of the American bullfrog at those temperatures.

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Suitability Index

0

10

15 20 25 27 Temperature (°C )

28

30

Figure 5 Suitability Index for American bullfrogs according to temperature

The final environmental variable considered for the HSI model is the Land Cover Type. American bullfrogs can survive in permanent ponds, but they capable of reproducing in temporal ponds. Wherever there are vegetation coverage and water, American bullfrogs seem to be generally present, although a particular association between distribution species and specific vegetation type have been reported (Guisan & Thuiller, 2005).

The land cover type dataset was extracted from the NLCD. The NLCD data is available from the Multi-Resolution Land Characteristics Consortium at www.mrlc.gov. Using the NLCD data and the literature information regarding the suitable land cover types associated with American bullfrogs, it was possible to create the SI for the land cover type.

Using the NLCD data and the literature information regarding the suitable land cover types associated with American bullfrogs, it was possible to create the SI for the land cover type. Ponds and bodies of open water were assigned with SI value for the land cover type equal to 1. Wetlands were assigned with an SI value equal to 0.8. Herbaceous vegetation was assigned with an SI value equal to 0.5. The evergreen, mixed and deciduous forest was assigned with an SI value equal to 0.2. The rest of the coverage land types (urban land, crops, among others) were assigned with an SI value of 0, as they 31

are not suitable habitats for the American bullfrog (Figure 6). The highest values correspond to habitats reported as suitable for the American bullfrogs. On the contrary, the lowest SI values were assigned to habitats where American

Suitable Index

bullfrogs have not been reported.

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Ponds Wetlands Herbaceous vegetation Forests

0

1

2

Other cover types 5 6

3 4 Land Cover Type

Suitability Index Figure 6 Suitability Index for American bullfrogs according to cover type

3.4.

Building the Suitability Index values using GIS

The following section presents the methodology used whit ArcMap to build the SIs for the three environmental variables. The outcomes from the process are shown in the Results chapter.

3.4.1. Suitability Index value for terrain elevation using GIS The terrain elevation SI for American bullfrog was calculated using the data of terrain elevation from the United States. It was necessary to regroup the intervals of terrain elevation within the values of SI presented in Figure 4.

The first step is to identify the baseline, lower, and higher terrain elevations thresholds, where the presence of American bullfrogs has been reported. The data rearrangement for each elevation thresholds was accomplished by using ArcMap Raster Calculator tool. The thresholds operations require multiple steps (Rasters procedures) to produce the SI for terrain elevation.

The baseline DEM was calculated as: đ??ˇđ??¸đ?‘€đ?‘?đ?‘Žđ?‘ đ?‘’đ?‘™đ?‘–đ?‘›đ?‘’ đ?‘Ąđ?‘’đ?‘&#x;đ?‘&#x;đ?‘Žđ?‘–đ?‘› đ?‘’đ?‘™đ?‘’

đ?‘Žđ?‘Ąđ?‘– đ?‘›

=

đ??ˇđ??¸đ?‘€

đ?‘&#x;đ?‘– đ?‘–đ?‘›đ?‘Žđ?‘™

1488.0

Equation 1 DEM base terrain elevation

− 1.0

32

First, it is necessary to subtract from the original value of DEM 1 meter to bring down the baseline to 0 meters as the lower terrain elevation. Then, it is divided by higher elevation value where American bullfrogs have been reported. In this case, the higher the elevation value is going to be equal to 1488 m (Bullfrog terrain elevation habitat range 1 meter - 1488 meters).

The next step is to determine where the DEM value is greater or equal to 1 meter and the where DEM is lower or equal to 1,489.0 meters to generate DEM areas that are not suitable habitats for the bullfrog. In Arc Map s raster calculator, the ampersand ( & ) character is used for a Boolean AND operation.

DEM for low elevation was calculated using the values of the original DEM as: đ??ˇđ??¸đ?‘€đ?‘™

= đ??ˇđ??¸đ?‘€

rr

đ?‘&#x;đ?‘– đ?‘–đ?‘›đ?‘Žđ?‘™

≼ 1.0 & đ??ˇđ??¸đ?‘€

Equation 2 DEM Low elevation

đ?‘&#x;đ?‘– đ?‘–đ?‘›đ?‘Žđ?‘™

≤ 1,489.0

DEM for high elevation was calculated using the values of the original DEM as: đ??ˇđ??¸đ?‘€

đ?‘–

= đ??ˇđ??¸đ?‘€

rr

đ?‘&#x;đ?‘– đ?‘–đ?‘›đ?‘Žđ?‘™

Equation 3 DEM High elevation

≼ 1,489.0

Finally using the baseline, low and high DEM the Suitable Index for elevation was calculated as: đ??ˇđ??¸đ?‘€

đ??ź

= đ??ˇđ??¸đ?‘€đ?‘?đ?‘Žđ?‘ đ?‘’

+ đ??ˇđ??¸đ?‘€

rr đ?‘–

rr

Ă— đ??ˇđ??¸đ?‘€đ?‘™

rr

Equation 4 Terrain elevation (DEM) Suitability Index (SI)

3.4.2. Suitability Index value for temperature using GIS Data of the maximum temperature recorded during each month of 2014 across the United States of America was downloaded from the PRISM Climate Group webpage. Temperature SI for the American bullfrog was calculated for the 12 months of the year 2014. The suitable temperature was reclassified into four groups using the Natural Breaks feature in ArcMap. Group number 1 set a temperature boundary at 18.9°C. Temperature values lower than 18.9°C are assigned a suitable Temperature Index (SI) equal to 0. Group number 2 cluster temperature values between 19°C to 27°C and assigned a suitable Temperature Index (SI) value of 1. Group number 3 set a temperature boundary 33

at 27.1°C. Temperature values higher than 18.9°C are assigned a suitable Temperature Index (SI) equal to 0. Finally, Group number 4 set a suitable Temperature Index (SI) equal to 0 to any temperature data point that is incomplete or empty. Table 3 presents an example of the Reclassify operation used for the month of January of 2014.

Table 3 Suitability Index for Temperature January

Group 1 2 3 4

Temperature -3°C to 18.9°C 19°C to 27°C 27.5°C No Data

Temperature SI 0 1 0 0

Temperature Index (SI) values for temperature for each month of 2014 were created using the layer code presented in Table 4.

Table 4 Layer code for temperature Suitability Indexes

Month January February March April May June July August September October November December

Layer code Reclass_Jan Reclass_Feb Reclass_Mar Reclass_Apr Reclass_May Reclass_Jun Reclass_Jul Reclass_Aug Reclass_Sep Reclass_Oct Reclass_Nov Reclass_Dec

3.4.3. Suitability Index value for the land cover type using GIS Using the NLCD from the United States, the SI for the land cover type was defined. Each land cover type was assigned a distinct SI value as it was previously defined in Figure 6. The data presented in the NLCD need to be clustered in a new layer following the SI values. However, the reclassification tool in ArcGIS was not capable of classifying the data using SI values ranging from 0 to 1. It was necessary to increase the scale of the original land cover type SI by ten units to complete the reclassification of the data. The 34

reclassification of land cover type was completed using the SI values presented in Table 5. To achieve this operation is necessary to use the Reclass tool. The result is a layer of data (SI đ?‘›đ?‘™đ?‘?đ?‘‘đ?‘?đ?‘˘đ?‘™đ?‘™ ) that cluster the five-land cover type with the respective SI for the American bullfrog in the U.S. Table 5 Land Cover Type SI

Land cover type Ponds Wetlands Herbaceous Vegetation Forests Other cover types

3.5.

Suitability Index 10 8 5 2 0

Building the Habitat Suitability Index

The HSI for the American bullfrog in the United States of America is calculated by multiplying the raster layers of the SI for terrain elevation, temperature, and the land cover type, following the flowchart presented in Figure 2. The final equation used to build the complete model for the HSI for a specific month of the year 2014 is described in Equation 5. The multiplication was performed for the twelve months of the year replacing the reclassification layer with the respective month temperature. đ??ťđ?‘Žđ?‘?đ?‘–đ?‘Ąđ?‘Žđ?‘Ą đ?‘ đ?‘˘đ?‘–đ?‘Ąđ?‘Žđ?‘?đ?‘–đ?‘™đ?‘–đ?‘Ąđ?‘Ś đ??źđ?‘›đ?‘‘đ?‘’đ?‘Ľ =

đ??ˇđ??¸đ?‘€đ?‘’đ?‘™đ?‘’đ?‘ đ?‘– ∗ đ?‘&#x;đ?‘’đ?‘?đ?‘™đ?‘Žđ?‘ đ?‘

Equation 5 Habitat suitability Index Equation

��

∗ đ?‘›đ?‘™đ?‘?đ?‘‘đ?‘?đ?‘˘đ?‘™đ?‘™

Next chapter summarizes the results for the SIs obtained for the three environmental variables terrain elevation, temperature, and the land cover type, followed by the HSI model generated outcomes for the twelve months of the year 2014.

3.6.

Animation for the Habitat Suitability Index

One of the advantages of applying GIS is the potential of presenting large volumes of complex information (data) using visual representations like maps. Using the resulting HSI maps for the twelve months of 2014, authorities can distinguish geographical areas suitable for the presence of the invasive American bullfrog. However, it would be confusing to the users to identify possible expansion patterns or particular behaviors of the American bullfrog 35

migration due to the changes in terrain elevation, temperature, and land cover type. As a viable alternative, an animation of the HSI model was incorporated to assist users in recognizing possible patterns in the data.

The animation was constructed using the resulting HSI maps. The maps were transformed to Tagged Image File Format (TIFF) files using ArcGIS. TIFF is a computer file format used for storing raster graphics images. One of the benefits of using TIFF files is the capability of performing image-manipulation while preserving high-quality images. Different software s can produce animations from a set of images. Nevertheless, as part of this work, an algorithm was implemented to automate the construction of the animation. The algorithm allows users to generate animations in Graphics Interchange Format (GIF) format using the TIFF images exported from ArcGIS. The user needs to define the path where the maps are stored. When the program starts, the user would be asked to identify the name of the file, the number of animation loops, and the delay time between image transitions. The final Matlab code is presented in Appendix A.

36

4. Results & Discussion The following chapter presents all the results produced during the construction of the HSI model for American bullfrog. The first subsection shows the outcomes for the SIs the terrain elevation, temperature, and the land cover type variables. The first subsection shows the results for the HSI model, followed by a discussion of the results.

4.1.

Suitability Indexes

Before the construction of the SIs, it is necessary to visualize the records of presence and observations registered for the American bullfrog across the United States of America.

4.1.1. Suitability Index (SI) for terrain elevation The first SI map presented in Figure 7 combines the data of registered bullfrog observations with the SI for terrain elevation. It is essential to notice that the bullfrogs seem to be present in large numbers on the Southwest coast and the Northeast coast of North America. The records of bullfrog observations presented in Figure 7 do not incorporate information regarding temperature, and date of the sampling. Additionally, these observation records show the presence of the bullfrog without considering the report regarding vegetation type or terrain elevation. However, the elevation in the terrain is a factor that restricts the areas where the invasive species can be found or can migrate.

When the layer of elevation SI is included to the map, a pattern can be identified. Areas with high terrain elevation do not show records of observations of bullfrogs, while regions with low terrain elevation show a high concentration of confirmed bullfrog observations.

37

Figure 7 Presence of American bullfrog in the United States & Suitability Index for terrain elevation

4.1.2. Suitability Index (SI) for temperature The SI for temperature for the American bullfrog in the United States change as the temperature change according to seasons. The following maps presented the resulting Suitable Indexes for temperature across the United States for the twelve months of the year 2014. January -Figure 8, February Figure 9, March - Figure 10, April - Figure 11, May -Figure 12, June - Figure 13, July - Figure 14, August - Figure 15, September - Figure 16, October Figure 187, November - Figure 198, December - Figure 19.

For example, during the summer period, the higher temperature reduces drastically the areas where the American bullfrog can have a suitable habitat.

Figure 14 shows the temperature SI values for July 2014. Areas in the far north and regions with high terrain elevation have a temperature SI value equal to 1.

When comparing this result with the outcomes generated for fall (November) of 2014 presented in Figure 18, it is possible to recognize drastic changes in the suitable habitats for the bullfrog. During fall, the areas at the far 38

south with low terrain elevation have an SI value of 1. Similarly, as winter is coming, and the temperature drops, areas with suitable temperatures between 19°C to 27°C will be distributed far south of the country.

39

Figure 8 Suitability Index for temperature - January 2014

40

Figure 9 Suitability Index for temperature - February 2014

41

Figure 10 Suitability Index for temperature - March 2014

42

Figure 11 Suitability Index for temperature - April 2014

43

Figure 12 Suitability Index for temperature - May 2014

44

Figure 13 Suitability Index for temperature - June 2014

45

Figure 14 Suitability Index for temperature - July 2014

46

Figure 15 Suitability Index for temperature - August 2014

47

Figure 16 Suitability Index for temperature - September 2014

48

Figure 17 Suitability Index for temperature - October 2014

49

Figure 18 Suitability Index for temperature - November 2014

50

Figure 19 Temperature Suitability Index - December 2014

51

4.1.3. Suitability Index (SI) for the land cover type The SI for the land cover type is presented in Figure 20 and allowed us to visualize the five types of land cover areas across the United States of America. The land cover type categories with their Suitable Indexes disclosed in Table 5 are exhibited in the resulting map.

52

Figure 20 Suitability Index for land cover type

53

4.2.

Habitat Suitability Index

The final result is a layer that combines the SI of the three environmental variables (terrain elevation, temperature, and the land cover type) to identify the possible locations in the United States where the American bullfrog can potentially have a suitable habitat. As presented in the Methodology section in Equation 5, by multiplying the SIs for terrain elevation, temperature, and the land cover, the HSI is created. Twelve operations were performed to achieve an HSI map for the American Bullfrog changes for the twelve months of 2014.

The resulting HSI maps present changes in suitable habitats using a color gradient. HSI with higher values, are presented in red colors, distinguish possible locations that are suitable habitats for American bullfrogs. Low HSI values are presented with blue colors to identify possible areas that distinguish possible locations that are not suitable habitats for American bullfrogs. As the American bullfrog is an invasive species, the use of the red color would identify areas that could be more likely to be invaded. Whereas, areas that are not suitable for the invasive species will present blue colors.

The following maps presented the resulting HSI for the three environmental variables across the United States for the twelve months of the year 2014.

The HSI map for January is presented in Error! Reference source not found.. The HSI map for February is presented in Figure 22. The HSI map for March is presented in Figure 23. The HSI map for April is presented in Figure 24. The HSI map for May is presented in Figure 25. The HSI map for June is presented in Figure 26. The HSI map for July is presented in Figure 27. The HSI map for August is presented in Figure 28. The HSI map for September is presented in Figure 29. The HSI map for October is presented in Figure 30.

The HSI map for November is presented in Figure 31. Finally, The HSI map for December is presented in Figure 32.

54

Figure 21 Habitat Suitability Index January 2014

55

Figure 22 Habitat Suitability Index February 2014

56

Figure 23 Habitat Suitability Index March 2014

57

Figure 24 Habitat Suitability Index April 2014

58

Figure 25 Habitat Suitability Index May 2014

59

Figure 26 Habitat Suitability Index June 2014

60

Figure 27 Habitat Suitability Index July 2104

61

Figure 28 Habitat Suitability Index August 2014

62

Figure 29 Habitat Suitability Index September 2014

63

Figure 30 Habitat Suitability Index October 2014

64

Figure 31 Habitat Suitability Index November 2014

65

Figure 32 Habitat Suitability Index December

66

4.3.

GIF Animation

Using the resulting HSI maps for the American bullfrog and the Matlab code a GIF animation was constructed. The animation can support users to visualize the changes on the habitat suitability across the United States as the temperature changes between the months of the year 2014. The GIF animation can be displayed in the following link HSI for American bullfrog GIF Animation.

4.4.

Discussion

The objective of this work was to identify possible areas in the Pacific Northwest of the United States of America vulnerable to the negative effects of the invasive American bullfrog using a HSI model. However, due to the shortage of confirmed observation data for the American bullfrog in this particular region (explained in the Methodology section), the HSI model was constructed for the entire United States of America. Excluding the States of Alaska, Hawaii, and the territory of Puerto Rico. Despite the importance of building efforts towards controlling and monitoring bullfrogs as invasive species, the information and data regarding its distributions and habitats are incomplete. Databases such as Global Invasive Species, USGS, or GBIF do not possess data concerning the amphibian s terrain elevation habitats and do not present information regarding the specific geographic location where the species was observed. In this work, HSI model for the American bullfrogs for the year 2014 was created. The outcomes of the model can support users and government agencies to distinguish areas that can be suitable habitats for the invasive species and take preventive measures to protect the native species and habitat. These identified areas reflect similar environmental patterns present in the natural habitats of the species (Figure 7). The results are preliminary evidence of the capability of the HSI models to determine species distributions. Unfortunately, the bullfrog observation records do not present information concerning the date of the sampling, preventing the comparison of 67

the HSI model simulations for each month with the actual records of observations of the species. To further validate the accuracy of the HSI model, more detailed data sampling and collection of bullfrog observations are need.

Nevertheless, the presented results are promising.

The HSI model created for the American bullfrog displays in the maps of the United States of America potential geographical locations where the invasive species can establish during the different seasons. In this work, as a proof of concept, the HSI model was completed using temperature data for the year 2014. Future work will examine the incorporation of more significant datasets, including temperature data for distinct years and data showing variation in the land cover type.

The current model is using one dynamic variable (temperature) and two static variables: terrain elevation and land cover type. The values of terrain elevation are not changing in time, in contrast to land cover vegetation that changes more often according to abiotic variables. Abiotic variables include physical conditions and non-living resources that affect the living organism in terms of growth, maintenance, and reproduction (Luan, Liu, Zhu, Wang, & Liu, 2012). Some examples of abiotic variables can include water, radiation, and humidity, among others. Different factors, such as temperature, time, and precipitation, can influence the dynamic behind the type and extension of vegetation in a particular area. Other factors affecting the land cover type are intrinsically related to such dynamic. For example, grasslands/herbaceous regions that are dominated by upland grasses are highly active as these areas are utilized for grazing. Incorporating the dynamic behind land cover changes will provide better approximations of areas that satisfactory meet the land cover types suitable conditions for the American bullfrogs. Additionally, future work can explore the use of different resources such as light detection and ranging (LIDAR), or combine satellite images from different years, refining the accuracy of the SI for land cover type in the areas of interest.

Temperature is the only dynamic variable considered in the HSI model.

Therefore, the changes in the HSI are only caused by seasonal variations in temperature. Higher temperatures result in an expansion of suitable areas. The 68

results showed how the during warmer periods, most of the regions located in the North of the United States became ideal for the habitat of the bullfrogs.

While, when the temperature decreases, suitable areas decreased significantly and are mostly located in the far south of the United States where temperatures are mild. As mentioned previously, the fact that changes in the model are coming from only one dynamic variable present a vulnerability for the accuracy of the results. Future work will investigate the interactions between distinct variables such as the variation of land cover type related to changes in temperature due to global warming.

The current model uses temperature data of 2014. The temperature data for the year 2014 is not a representative, and it was selected randomly. If more records of temperature data were incorporated, it would be possible to identify patterns in specific areas year by year. This would provide an advantage to the user, who could analyze and compare temperatures of different years for a particular month or season. As a result, it would be possible to narrow down the examination of sensitive locations where local species are more vulnerable to invasive species. By analyzing the temperature changes for each month of the year, the model could identify migration patterns of the American bullfrog.

In comparison, using the average year temperature would not allow us to understand or identify such migration patterns.

The HSI model was able to identify possible locations for the invasive species using three environmental variables. The presented results complete the objective of the project inside the time and scope of development of the project successfully. The next steps for the HSI model would be the inclusion of more environmental factors. For example, considering factors such as precipitation would increase the detail of the model without increasing the complexity of the methodology resulting in efficient processing of information.

It is highly recommended to pursue on-site validation of the results obtained from the HSI model. The presented simulation identified possible locations where the environmental parameters match the habitat requirements of the American bullfrog. Field validation exploring the designated areas to confirm

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the presence of the invasive species is required to prove the capability of the model further.

Biological information of the American bullfrog as an invasive species was not used to build the Habitat Suitability Index model. Future work needs to incorporate biological data about reproduction cycles, migration routes, and feeding patterns, invasion trends, etc. Adding biological and environmental data to the model will improve the fidelity and accuracy of the model since the model will be considering variables that are related to the behavior of the species with the environment. It will be essential to analyze the interaction of American bullfrogs with other species. For example, there are reports that invasive fishes can facilitate the distribution of American bullfrogs in Oregon by increasing tadpole survival rates (Adams & Pearl, 2007). The invasive fish is reducing predatory populations of insects such as dragonflies meaning a decrease in the predatory pressure of the invasive tadpoles and young metamorphs. It would be possible to combine HSI models for different invasive species to study and investigate how the interaction between species could be related to invasive expansions.

The results of the presented model are using the Suitable Indexes for reported distributions of American bullfrogs in their native habitat. The model is assuming that there is no difference between invasive and native distributions of American bullfrogs. However, invasive populations of American bullfrog in the northwest of the United States of America might not necessarily follow the SIs defined by the native populations of American bullfrog. There is a possibility that the invasive species in the northwest could have adapted to the new environmental conditions (elevation, temperature, type of vegetation). Future work needs to consider this possibility and gather more records from the invasive species. The presented HSI model can be used as a visualization tool.

The implementation of animation is an additional trait of this work that supports users to quickly detect the patterns or variations in the results HSI model related to temperature changes during a specific year.

One of the main challenges of the development of this project was the absence of consistent format on the variables data. Many data points of 70

presence of American Bullfrog could not be used due to errors in the data information. Using standardized information is the first step to create proposals about the management of invasive species and the conservation of native amphibian species. Having information well organized (format, and form) would allow fast analysis. Keeping a standardized format would allow connecting geographic information with biological information more straightforward and more efficient.

It is necessary to discuss the importance of temporal and spatial scale for studies in biological invasions. The proper scale must be defined according to the purpose of the analysis and the species being evaluated. In the presented case study, the objective was to analyze areas that could be suitable habitats for the invasive American bullfrogs in the Pacific Northwest of the United States.

The limitation on spatial case seemed appropriate at the time. However, as the study area was delimited and records of the species were collected, some records available were inoperable. In this particular case study, the nonexistence information about the species on the proposed area of analysis limited the results of the HSI model. As a result, it was necessary to expand the original spatial scale from the Pacific Northwest to the entire United States of America.

The temporal scale for this study used temperature data for the year 2014 for the entire country. However, the values of temperature used are an average for overall month at gross locations. If the HSI model wants to secure located areas suitable for the habitat of the invasive species, is required to use precise temperature data on the specific site under study over more extended periods of time. Future work on the model needs to collect and use data of temperature for the spatial scale over long periods.

One remarkable result obtained from the HSI model is the geographical location of the possible suitable habitats for the American bullfrog. By studying the patterns displayed in the animation, one can conclude that suitable habitats for the bullfrog are not present on the East coast of the United States of America. The bullfrog is a native species of the East coast, it could be possible that the bullfrog is migrating towards the West coast due to a loss of its natural 71

habitat. The limited biological information of the amphibians migration patterns and hibernation process restrict the capability of the experts to understand the behavior of the bullfrog. Further field expeditions need to confirm the presence of bullfrog in both coasts to verify the results of the model.

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5. Conclusions & Recommendations In this work, a HSI model for the American bullfrog in the United States of America was developed. The American bullfrog is an invasive species that have spread across the United States and represents a threat for endemic species and habitats, especially in the PNW. The implementation of the HSI model has the potential of supporting researchers and governmental agencies to distinguish vulnerable areas and make informed decisions towards implementing mitigation procedures.

As part of this different research SDMs were examined and compared.

The HSI model was selected because it has the advantage of creating accurate models faster and is capable of incorporating the hypothesis of the specieshabitat relationship that can be evaluated with field observations.

The HSI model was constructed using three environmental variables terrain elevation, temperature, and the land cover type. For each environmental variable, a SI was defined to describe the conditions that make a habitat suitable for the American bullfrog. A SI with a value equal to 1 indicates parameters suitable for the habitat of the American bullfrog, while SI values equal to 0 indicates parameters not ideal for a habitat.

Using the classification tools of ArcGIS the data of the environmental variables were clustered to build the SIs. The final HSI model was constructed by the multiplication of the raster layers of the SI for terrain elevation, temperature, and the land cover type. The HSI models were created for the twelve months of the year 2014. Finally, to facilitate the interpretation of the results, an animation was constructed using the resulting HSI maps.

The main contribution of this work is the rapid implementation of a HSI model to simulate the possible distribution of the American bullfrog across the United States of America. The objective of this work was completed successfully. The outcome of the model distinguishes potential areas where environmental factors are suitable for the presence of the invasive species.

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Nevertheless, this model is an approximation, and field observations are required to validate the results. Additionally, the current HSI model just uses three independent variables. Adding more variables to the model can refine the significantly the output. Future work needs to explore the incorporation of more variables such as precipitation, biological information about the bullfrog, among others.

To perform a validation of the HSI model is recommended to take into account the suggestions mentioned before (inclusion of more variables) to generate a suite of models to be evaluated. For this particular case study, comparing the results from the generated HSI model with new records of presence for the American bullfrog in the identified areas would corroborate the accuracy of the presented HSI model.

HSI models can serve as robust spatial tools to inform and take decisions about species management. The preliminary results can be used to select sampling sites. The validation of the method can be estimated according to the data of presence and absence recollect for the invasive species on the chosen areas. The results of this work confirm the generated hypothesis.

Habitat Suitable Index model can be used to identify possible areas suitable for invasive species. The generated maps will provide information that can be used to create contingencies plan and control measures to protect local species.

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Appendix A. Animation Code The following Matlab code creates an animation using image files. The code in the program creates a slides show from a set of images and save it as an animated GIF file. To use the code, you need to copy the following lines into a new m file and save it. clear all [file_name file_path]=uigetfile({'*.jpeg;*.jpg;*.bmp;*.tif;*.tiff;*.png;*.gif','Image Files (JPEG, BMP, TIFF, PNG and GIF)'},'Select Images','multiselect','on'); file_name=sort(file_name); [file_name2 file_path2]=uiputfile('*.gif','Save as animated GIF',file_path); lps=questdlg('How many loops?','Loops','Forever','None','Other','Forever'); switch lps case 'Forever' loops=65535; case 'None' loops=1; case 'Other' loops=inputdlg('Enter number of loops? (must be an integer between 1-65535) .','Loops'); loops=str2num(loops{1}); end delay=inputdlg('What is the delay time? (in seconds) .','Delay'); delay=str2num(delay{1}); dly=questdlg('Different delay for the first image?','Delay','Yes','No','No'); if strcmp(dly,'Yes') delay1=inputdlg('What is the delay time for the first image? (in seconds) .','Delay'); delay1=str2num(delay1{1}); else delay1=delay; end dly=questdlg('Different delay for the last image?','Delay','Yes','No','No'); if strcmp(dly,'Yes') delay2=inputdlg('What is the delay time for the last image? (in seconds) .','Delay'); delay2=str2num(delay2{1}); else delay2=delay;

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end h = waitbar(0,['0% done'],'name','Progress') ; for i=1:length(file_name) if strcmpi('gif',file_name{i}(end-2:end)) [M c_map]=imread([file_path,file_name{i}]); else a=imread([file_path,file_name{i}]); [~,~,ch] = size(a); if ch == 1 % 'a' is a grayscale image

a = repmat(a, [1, 1, 3]); end [M c_map]= rgb2ind(a,256); end if i==1 imwrite(M,c_map,[file_path2,file_name2],'gif','LoopCount',loops,'DelayTime',delay1) elseif i==length(file_name) imwrite(M,c_map,[file_path2,file_name2],'gif','WriteMode','append','DelayTime',delay2) else imwrite(M,c_map,[file_path2,file_name2],'gif','WriteMode','append','DelayTime',delay) end waitbar(i/length(file_name),h,[num2str(round(100*i/length(file_name))),'% done']) ; end close(h); msgbox('Finished Successfully!')

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