Genetic diversity and distinctiveness of Plectrohyla guatemalensis (Anura: Hylidae) in Guatemala
A thesis submitted to The University of Manchester for the degree of Mphil Environmental Biology in the Faculty of Life Sciences
2014
Olga Alejandra Zamora Jerez Contents
Contents ...... 2 List of Tables ...... 3 List of Figures ...... 3 Abstract ...... 4 Declaration ...... 5 Copyright ...... 5 Dedication ...... 6 Acknowledgements...... 6 Introduction ...... 8 Amphibians and Ecosystem Services ...... 9 Amphibians and regulation functions...... 10 Amphibians and Production Functions ...... 12 Amphibians and Information Functions ...... 14 Conservation Genetics of Amphibians and Microsatellites ...... 15 Amphibians of Guatemala: Plectrohyla guatemalensis ...... 17 Aims of the project ...... 21 Methods ...... 22 Field survey ...... 22 DNA extraction ...... 25 Microsatellite Primer development ...... 26 DNA amplification ...... 27 Microsatellite Genotyping ...... 28 Data Analysis ...... 28 Results ...... 31 Field Surveys and population status ...... 31 Microsatellite development ...... 31 Population genetics of Plectrohyla guatemalensis ...... 39 Discussion ...... 42 Population status of Plectrohyla guatemalensis ...... 42 Microsatellite development ...... 44 Preliminary Population Genetics Analysis of Plectrohyla guatemalensis ...... 47 Conservation and the future ...... 48 References ...... 51 Appendices ...... 60
2 List of Tables
Table 1 Localities where P. guatemalensis surveys were conducted ...... 25 Table 2 Multiplex PCR tested for the P. guatemalensis samples ...... 30 Table 3 Sample size, Number of different alleles, Allele range and null allele frequency for the three populations analyzed ...... 33 Table 4 Observed heterozygosity, expected heterozygosity , Inbreeding index, P-value for HWE and allelic richness for the three populations analyzed ...... 40 Table 5 Complete set of primers tested for in this project ...... 60 Table 6 STRand results for the nine loci ...... 62 Table 7 P-values for the Hardy-Weinberg Analysis ...... 66 Table 8 AMOVA results per loci for the three populations ...... 66 Table 9 Allele range for each locus ...... 66 Table 10 Localities, coordinates and samples collected...... 67
List of Figures
Figure 1 Map of the endemism zones proposed by Schuster et al. (2000)...... 18 Figure 2 Map of the distribution of P. guatemalensis in Guatemala...... 19 Figure 3 Plectrohyla guatemalensis collected in the forests of Laj Chimel, Quiche...... 20 Figure 4 Map of the localities where P. guatemalensis surveys were done...... 24 Figure 5 Allele frequency distribution for each locus...... 38 Figure 6 Allelic patterns across the three populations ...... 39 Figure 7 STRUCTURE bar plots for the whole data set (K=3)...... 41 Figure 8 Investigation license extended by CONAP ...... 80 Figure 9 Collection license extended by CONAP ...... 81 Figure 10 Exportation license extended by CONAP ...... 82
Word count: 23,284
3 Abstract
The University of Manchester Olga Alejandra Zamora Jerez MPhil Environmental Biology Genetic diversity and distinctiveness of Plectrohyla guatemalensis (Anura: Hylidae) in Guatemala 2014
Microsatellite markers are acquiring more attention for the study and conservation of populations. They can help scientists understand and ask a variety of questions about populations. Next Generation Sequencing technologies (NGS) are becoming the preferred method for scientists to develop microsatellites since they can produce large amounts of data in less time than conventional cloning. P. guatemalensis is a hylid frog that inhabits forests of Guatemala, México and Honduras. In this project microsatellite markers were developed using both methods to do a first assessment on the genetic status of P. guatemalensis in Guatemala.
Microsatellites primers were developed using both conventional cloning techniques at Sheffield University (SHF) and NGS. Twenty one pairs of primers were developed using data generated from an Illumina Miseq sequencer while 16 primers were developed using cloning methods in Sheffield. In total a set of eight polymorphic loci were used to evaluate the population genetics of P. guatemalensis.This study shows the use of NGS technologies and bioinformatics tools to be a quick and effective way of obtaining Possible Amplifiable Loci (PALs). In this project only a few loci could be tested and more experiments are needed before being able to determine if one method is advantageous over the other.
Results showed that the observed heterozygosity was higher than expected for seventeen loci across all populations, and divergence from Hardy Weinberg Equilibrium was found in seven loci in Laj Chimel and Tatasiriré populations and four loci from Trifinio. The inbreeding coefficient (FIS) varied from -0.578 to 0.633 across populations. The negative value for some loci suggests possible outbreeding, however, other loci show a strong positive value for Fis potentially indicating small scale substructure within populations. FST showed clear population differentiation between the three localities, with a value of 0.302.
The localities were P. guatemalensis was found are in danger of disappearing entirely due to habitat loss, pollution and emergent infectious diseases. It is important to do constant surveys in order to plan properly the conservation of amphibian species in these threatened forests. The microsatellites developed during this study will be useful for future of conservation genetics programs of amphibians in Guatemala.
4
Declaration
I Olga Alejandra Zamora Jerez declare that no portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.
Copyright
i. The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has given The University of Manchester certain rights to use such Copyright, including for administrative purposes. ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made. iii. The ownership of certain Copyright, patents, designs, trade marks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the thesis, for example graphs and tables (“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions. iv. Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=487), in any relevant Thesis restriction declarations deposited in the University Library, The University Library’s regulations (see http://www.manchester.ac.uk/library/aboutus/regulations) and in The University’s policy on Presentation of Theses.
5
Dedication
I want to dedicate this work to my grandmas Ely, Olga and Sofia (RIP). Thanks for being the best example what can be achieved with hard work and dedication. I love you!
Acknowledgements
I want to thank to my Supervisor Dr. Catherine Walton for giving me the opportunity of being part of her lab and all the guidance during the project. I also want to acknowledge Professor Richard Preziosi for all the support and the opportunities. To the Faculty of Life Sciences who gave me the opportunity to study here through a scholarship that funded both myself and the project without which it would not have been possible.
I want to thank to my family who were always there from the distance, special thanks to my parents and siblings for supporting and believing in me, you have no idea how much I appreciate every effort you do for me, I would not have accomplished this without you. ¡Los quiero a morir!
Special thanks to Milena Oliva and Erick López for all the help, the late night chats, the memories and the great company during the field trips and the work in Guatemala, you are the best herpetologist and friend one could ever ask. Lucía García and María Fernanda Villagran for getting me in contact with the communities and joining me in some field trips. Flor Ruiz, Priscila Juárez, Elizabeth Pellecer, Juan Diego Chang, Juan Manuel Pérez, Julio Granados and all the lovely people from Universidad del Valle de Guatemala (UVG) that where always willing to give a hand. To Dr. Erick Smith for the guidance and advise.
I want to thank the following people and institutions for helping and supporting me during different steps of the field trips: Consejo Nacional de Áreas Protegidas (CONAP) and Franklin Herrera for conceding the research, collection and exportation licenses and supporting the project; Helvetas Swiss Cooperative, people from Caserio Buenos Aires,
6 Maximino and Francisco Mazariegos; Sibinal Municiality and Elfido Pérez; Renardo Ovalle and all the workers from Finca La Bolsa; The Martínez family from Finca El Injerto for making us feel like we were at home; Los Tarrales Private Nature Reserve, Andy Burge and Cristina Vasquez; People from Asociación para el Desarrollo Integral de Chimel (ASODICH), especially to Alejandro López Us, Elias Barrera, Doña María and the Us family for always opening the doors of your house to us and the amazing caldo de gallina; Plan de la Arada Community, Milton Solis and Cirilo Súchite; Tatasiriré Waterfalls Park, Mariano Portillo and the Barrera family; Alejandro González Madrid for all the contacts and help in the field; Dr. Phillip Tanimoto and Dr. Tim Hatten for introduce me to the magical forests of Laj Chimel.
To the Biology Department at UVG, Licenciada Margarita Palmieri and Dr. Jack Schuster for allowing the use of the University facilities and always being willing to support me.
To the Walton’s lab people, thanks for let me be part of the team, also to Nathan Truelove for all the advice. Big thanks to Liliana Solano and Thomas Hughes for all the company, the late lab nights and for always being there with a good advice. Thanks to the Hughes family for all the help and advice during the process. To Peter Brigs at the informatics unit, for always being willing to answer my questions. Renia Kidona thanks for opening the doors of your house.
7 Introduction
The important role that amphibians play within diverse ecosystems has been somewhat ignored. The value that they provide from food provision, medical products, bio-control (pest and disease vector insect), cultural services (art, music, rituals) and scientific research cannot be underestimated. Several studies have pointed out the importance of amphibians and the impact that their disappearance would have, not only inside their ecosystems (Whiles et al. 2006), but also for human well-being (Crump 2010; Krieger 2009).
Tropical regions have the highest level of diversity within amphibian species however, in recent times the populations have been declining for a number of reasons ranging from accelerated habitat destruction, change in land use, pesticide pollution, climate change and diseases (Berger et al. 1998; Blaustein & Kiesecker 2002; Mendelson III et al. 2004; Stuart et al. 2004; Lips et al. 2005; Rovito et al. 2009). In Guatemala, studies have shown declining populations in some areas (Mendelson III et al. 2004; Rovito et al. 2009; Cheng et al. 2011). The country has amphibian conservation initiatives, but none of these efforts has focused on the genetic status of populations, and most of them have been done on Plethodontidae salamanders (e.g. Parra-Olea et al. 2004; Rovito et al. 2009; Rovito et al. 2011).
In 2010 IUCN catalogued P. guatemalensis as critically endangered (Santos-Barrera & Canseco-Márquez 2010); this highlights a special need to involve genetic tools to give a better understanding and knowledge of the genetic resources and the dynamic within, and between populations, in order to improve conservation management plans. The microsatellites developed in this project were the first developed for P. guatemalensis. The results are one of the first steps for population genetic studies of Guatemalan amphibians, as well as a starting point for the development of suitable conservation plans for amphibians as important genetic elements inside the ecosystems and to understand the role that genetic diversity can play in ecosystem services.
8 Amphibians and Ecosystem Services
Ecosystem services are all the goods and services that come from natural systems that are useful for humans (De Groot et al. 2002; Wallace 2007). They can satisfy humans directly or indirectly, not only for survival (air purification, biological control, carbon sequestration, source of food, climate regulation) but also culturally and aesthetically (Naido et al. 2008; Kremen 2005; De Groot et al. 2002).
The services have been grouped in four categories: Regulation functions (e.g. climate regulation, clean air and water), habitat functions (e.g. refuge for different organisms), production functions (e.g. products obtained by nature like food and medical resources) and information and cultural functions (e.g. cultural, aesthetic and educational experiences) (De Groot et al. 2002, Millennium Ecosystem Assessment 2005). The Millennium Ecosystem Assessment (2005) documented the importance of these services to humans and showed how human activities are the major threat for these services (Balmford 2002; Naido et al. 2008).
Amphibians play an important role not only inside their habitat but also for human well being, economics and culture. Since they are very susceptible to environmental changes they are good bio indicators of the health of the ecosystem (Lee 2000). They provide, and are part of important ecosystem services. They can be related to three of the four categories proposed by De Groot et al. (2002): regulation, production and information functions.
Research has demonstrated that the decline of amphibians can affect the function of these services, consequently affecting human well being (Crump 2009; Krieger 2009). It is therefore important that scientists start to pay more attention to the relationship between these organisms and the services that they provide.
9
Amphibians and regulation functions:
Regulation functions are the services provided by natural ecosystems that help to regulate essential ecological processes. These have several benefits to humans, like clean air and water and the control of biological processes (De Groot et al. 2002).
Biological control
One of the main services that amphibians provide is biological control since they are predators of agricultural pest and insect disease vectors (Kriger 2009; Crump 2010; Valencia-Aguilar et al. 2013). Plagues cause large economic losses in agriculture each year resulting in reduced production as well as the large amount of investment needed for pest control. During the last decade integrated pest management approaches have gained more attention in their desire to combat the problem by implementing natural and cheaper systems (Valencia-Aguilar et al. 2013). For example, in Argentina studies have found that amphibians can be important biological controllers for the transgenic soya cultivars (Hartmann et al. 1999 in Attademo et al. 2007). Species from the families Bufonidae, Cycloramphidae, Leiuperidae and Leptodactylidae feed on different plague insects, and there is a positive trend between the percentage of amphibians in the cultivars and the height of the plants (Attademo et al. 2007; Peltzer et al. 2010). Maintaining amphibian populations and understanding how they interact with their prey can be useful to farmers, using them as biological control can minimize economic and environmental costs (Bellows 2001; Blaustein & Chase 2007).
The control of human disease vectors is one of the most important service they provide for human beings, especially in the tropics where diseases like yellow fever, West Nile virus, dengue and malaria affects a lot of people, especially the ones living in rural areas without adequate medical assistance. It has been shown that tadpoles of Osteopilus septentrionalis and Lysapsus limellus eat flies from the family Ephydridae, which are insects are associated with human disease (Spielman & Sullivan, 1974 in Valencia-Aguilar et al. 2013; Peltzer & Lajmanovich 2002).
10
Nutrient Cycling
Amphibians play important roles in nutrient cycling by being part of several food chains, both as prey and predators of a wide range of organisms (Crump 2010; Valencia-Aguilar et al. 2013), and transferring the nutrients to aquatic and land systems (Crump 2010), a vital and unique service this taxa provides to the ecosystem.
They can also digest materials, like chitin, making the nutrients accessible for other animals (Burton & Likens 1975 in Valencia-Aguilar et al. 2013). Frogs are ectothermals, so they do not use a lot of the energy for their activities; they ingest food in order to maintain their body temperature. Unlike mammals and birds (that use up to 98% of the ingested energy), amphibians convert about 50% of their energy gained from food into new tissue, which is then transferred to the next level in the food chain (Pough et al. 2004 in Crump 2010).
It has been recorded that tadpoles in general can contribute to the nutrient cycle by being primary consumers, influencing the densities and structure of algae populations, and the amount of detritus inside the system (Ranvestel et al. 2004; Whiles et al. 2006; Mohneke and Rödel 2009). There is evidence that removing or reducing the tadpole populations can alter the ecosystem by altering the nutrient cycling, the primary production, detritus decomposition, and invertebrate populations (Ranvestal et al. 2004; Flecker et al. 2009; Kiffney & Richardson 2001 in Whiles et al. 2006; Mohneke & Rödel 2009). The absence of these frogs from the natural system can decrease the amount of potassium and phosphorus available for leaf litter decomposition; both essential nutrients for the microbial activity and the growth of plants (Beard et al. 2002; Beard et al. 2003). They help to reduce natural eutrophication (Ranvestal et al. 2004; Crump 2010). There is also evidence that they can cooperate or facilitate the way for other grazers like mayflies (Ranvestal et al. 2004), by exposing the food resources (Whiles et al. 2006).
It has been recoreded in Puerto Rico that the Coqui frog (Eleutherodactylus coqui) helps to increase the rate of nutrient cycling in the system by the deposition of feces, urine, and carcasses and its movement between aquatic to land systems (Beard et al. 2002: Beard et al. 2003).
11 Connelly et al. (2011) has found that tadpoles of Espadarana prosoblepon, Sachatamia albomaculata, Hyalinobatrachium colymbiphyllum, and Centrolene spp. can simulate fungal activity in aquatics systems and are important for nutrient cycling. It is also believed that through their excretions they can increase the concentration of nutrients in the system.
On land amphibians can help controlling arthropod populations, at the same time they are eaten by snakes, bats, mammals, and wasps, fish and water arthropods in the case of tadpoles (Mohneke & Rödel 2009). If the frogs disappear there is a high risk for the predators as well. There are snake species that specialize in frogs and their eggs. Whiles et al. (2006) found that since stream-brooding frogs in El Copé forest, Panama are presenting population declines, some riparian frog-eaters snakes have not been seen during whilst completing surveys. This shows the impact that the loss of a link can have on the whole chain and the species composition on both land and aquatic ecosystems.
Amphibians and Production Functions:
Food sources
The most direct economic service provided by amphibians is the use as food, with one thousands of amphibians commercialized (Tyler et al. 2007). Frogs are used as food sources both in small rural communities and in large city restaurants where they are presented as great delicacies (Gratwicke et al. 2009; Mittermeier et al. 1992 and Thorbjarnarson 1991 in Valencia-Aguilar et al. 2013). In some small towns in Central America, anurans from the genera Lithobates are used as a food source (Marineros 2007 in Valencia-Aguilar et al. 2013). In Caribbean islands they use frogs from the Leptodactylus genera (Valencia-Valencia-Aguilar et al. 2013), probably because of their large size. Although they are consumed worldwide and their direct value is clearly substantial, the extent of the international trade in frogs as food is unknown (Gratwicke et al. 2009).
12 Raw materials and trade
Amphibians are also sold as pets. A great many species are exported from Latin American countries to North America, Europe and Asia (Thorbjarnarson 2000 in Valencia-Valencia- Aguilar et al. 2013). Frogs from the Hylid and Dendrobatidae family are the ones that are in most demand, especially the Dendrobates and Agalychnis genera (Valencia-Valencia- Aguilar et al. 2013) for their attractive colours. A great problem involving the trade and movement of frogs from one place to another is the dispersal of diseases.
Medicinal resources
Amphibian skin contains granular glands that secrete substances containing chemical compounds that protect them from pathogens and predators. Scientists have isolated and studied the compounds of these secretions to develop medical products for human and veterinary uses (Tyler et al. 2007; Clarke 1996 in Valencia-Aguilar et al. 2013). The compounds include biogenic amines, bufodienolides (bufogenines and bufotoxines), alkaloids, peptides, and proteins (Daly et al. 1987; Clarke 1996; Wells 2007 in Valencia- Aguilar et al. 2013).
In Central and South America some communities use amphibians for medicinal purposes. The products are obtained mainly from the skin and fat of species from the genera Rhinella and Leptodactylus (Vázquez et al. 2006; Cuesta-Ríos et al. 2007; Alves et al. 2009; Valencia-Aguilar et al. 2013). Studies have found compounds that contain alkaloids that in combination with antibiotics can enhance the effects of the drugs, showing that these extracts can be important natural resources for the production of enhanced antibiotic drugs (Santos et al. 2011 and Santos et al. 2012 in Valencia-Aguilar et al. 2013).
An example of how much human beings can lose with amphibian declines is the extinction of Rheobatrachus silus, the Australian gastric brooding frog. The females swallowed the fertilized eggs and the young grew inside the stomach until they were completely developed (Tyler & Carter 1981). Studies (Tyler et al. 1983; de la Lande et al. 1984) found that prostaglandin E2 secreted from the eggs and larvae was the substance that inhibited the
13 gastric acids secretion from the stomach, enabling the larvae to develop completely. Scientists believe that this knowledge could have been used medically, especially in the treatment of gastric ulcers. Unfortunately these frogs disappeared before scientists could determine how this process worked.
Amphibians and Information Functions:
Science and teaching
Amphibians are used in academic activities for teaching purposes in high schools and universities as well as in different scientific research, either as model organisms or as the interest species (Crump 2010). Approximately 10% of the Nobel prizes in physiology and medicine have resulted from investigations that have used frogs (Tyler et al. 2007; Kriger 2009). They can be of use in scientific research either as model organisms or as a source for potential medical compounds.
Cultural and Aesthetic
Amphibians have a cultural and intrinsic value. In some cultures they represent good luck and fertility because they are associated with rain (Crump 2010; Valencia-Aguilar et al. 2013). Hunters in some Amazon tribes put frog secretions in scarifications on their arms, as they believe it improves their hunting skills (Valencia-Aguilar et al. 2013). They have also been used as weapons by native people of South America, as is the case of the dart frogs, where venom was used in the tips of the weapons to ensure the death of their enemies (Crump 2010; Kriger 2009; Valencia-Aguilar et al. 2013).
Though we can find a lot of valuable and measurable services that amphibians provide, their intrinsic services are as valuable as the first. The cultural rituals that involve amphibians are found in several communities. Also they are used for inspiration in the art and music world (Crump 2010), they are also used as ornaments as well as exotic pets
14 (Kriger 2009; Valencia-Aguilar et al. 2013). This shows the importance amphibians have, not only for small towns or cultures that use them in their rituals, but for the entire human race.
In some part of the tropics, amphibians can comprise the most abundant land vertebrates (Stebbins & Cohen in Whiles et al. 2006), and they are involved with both aquatic and land habitats. Research done in Central America has demonstrated that stream-breeding frogs are one of the most endangered groups and this can have a lot of consequences for the aquatic ecosystems (Whiles et al. 2006). P. guatemalensis is a stream-breeding frog, which can contribute to several services inside the forest.
Conservation Genetics of Amphibians and Microsatellites
The decline of amphibian populations is a serious issue, and it is crucial to include population genetic and diversity studies in conservation plans (Collins & Storfer 2003; Storfer 2003). They are the most endangered animal group on the earth. Almost one third of the 6,485 species in the world are in danger and at least 150 have disappeared in the last decade (Kriger 2009). These declines are associated with habitat destruction, change in land use, global warming trading and diseases, especially Chytridiomicosis that have already caused extinctions in Central America and Australian populations (Stuart et al. 2004; Lips et al. 2005; Whitfield et al. 2007; Kriger 2009; Crump 2010).
In Guatemala some conservation efforts have been made to protect endangered amphibian species. Amphibian declines has been associated with habitat destruction, change in land use (Rovito et al. 2009), pesticide pollution (Mendelson III et al. 2004), and more recently to infectious diseases (Rovito et al. 2009; Zamora unpublished data). By comparing field records from the 70 and 80s with more recent data there is a clear pattern in terms of population declines (pers. comm. Jacobo Conde). It is important, due to the rapid loss and contamination of ecosystems, that herpetologists focus their studies on the active management of populations throughout their natural distribution, not only in trying to preserve them in close reserves or refugees (Jehle 2010).
15 With the new advances in molecular technologies genetic markers are useful tools that can be applied to conservation (Jehle 2010). Microsatellites (single sequence repeats, SSRs) are abundant molecular markers that are useful for studying the genetics of populations since they have a high mutation rate per locus, per generation (Selkoe & Toonen 2006), which makes them particularly informative of recent events, which are often the most relevant for conservation studies (Enalik et al. 2012). By detecting multiple alleles per loci researchers can gain a lot more information per locus using microsatellites than other markers, such as single nucleotide polymorphism (SNP) (Hamblin et al. 2007; Enalik et al. 2012).
Microsatellites can be very useful in studying ecology because they can help researchers to answer different genetic and ecological questions by complementing standard field methods (Jehle & Arntzen 2002; Selkoe & Toonen 2006). Microsatellites have been developed for several amphibians (e.g. Garner et al. 2000; Rowe et al. 2000; Garner & Tomio 2001; Rowe & Beebee 2001; Van Den Bussche et al. 2009; Velo Antón et al. 2009; Barrat et al. 2011; Mochida et al. 2011; Gutiérrez-Rodríguez & Martínez-Solano 2013). To our knowledge however, no microsatellites have yet been developed for Plectrohyla.
Genetic approaches can be useful when new conservation studies are being planned, since they can give information about the status of the population (Storfer 2003), with all the applications they have they can help scientist to make better decisions in how to solve potential conservation issues (Hedrick 2001). Genetic approaches can be use to determine if the population should be divided into conservation units, which is an important first step in conservation (Funk et al. 2012) also they have been used to determine spatio-temporal structure of (Calboli et al. 2011), measuring gene flow, effective population size and detecting bottlenecks in amphibian populations (Jehle & Arntzen 2002).
Nevertheless it is important to realize that in order to conserve amphibian populations it is not enough to gather a lot of genetic information if it is not integrated and used in conservations plans (Jehle 2010). Knowing the conservation status of the species can help not only to conserve the species as a group, but also the ecosystem as a system and therefore the ecosystem services (Valencia-Aguilar et al. 2013).
16 Nowadays next-generation sequencing (NGS) technologies can be used for a lot of applications; not only in the fields of molecular and genetic biology, but also in conservation biology and ecology (Huang et al. 2013). The two major NGS technologies with emergent application in single sequence repeats isolation are 454 and Illumina (Enalik et al. 2012), these technologies can replace the need to make microsatellites libraries and are a faster method to obtain microsatellite loci for different species (Castoe et al. 2010; Castoe et al. 2012a; Castoe et al. 2012b; Zhang et al. 2011; Drechsler et al. 2013).
Researchers (Castoe et al. 2010; Castoe et al. 2012a; Castoe et al. 2012b; Zhang et al. 2011; Drechsler et al. 2013) are using next-generation sequencing, since these technologies are easier, more reliable and less costly than traditional ones. These methods also have the advantage of producing large amounts of data, from which microsatellites can be developed (Castoe et al. 2012a; Castoe et al. 2012b; Enalik et al. 2012, Drechsler et al. 2013). Castoe et al. (2012a) used the Illumina paired-end sequencing to identify microsatellites in birds and snakes, they found out that this technology was capable of identifying a great number of Potential Amplifiable Loci (PALs), even in the case of birds that have low genomic microsatellite densities. Producing great amount of data can also be an advantage in the case of large genomes present in some amphibians (Garner 2002; Jehle 2010; Enalik et al. 2012).
Amphibians of Guatemala: Plectrohyla guatemalensis
Guatemala has a great amphibian biodiversity, having a hundred and sixty two species of amphibians, distributed in three orders, eleven families and thirty-six genera (Acevedo 2006; Amphibiaweb 2013). It is the group with the highest number of endemic species with 25 species of frog, 19 salamanders and a caecilian (Acevedo 2006), and these numbers keep increasing. As has previously been stated, amphibian decline is a major issue worldwide and in Guatemala this decline has been recorded since the 1980s. This has been attributed to habitat destruction (Rovito et al. 2009), global warming and recently to infectious diseases (Rovito et al. 2009; Zamora unpublished data).
17 Frogs of the genus P. guatemalensis (HYLIDAE) are restricted to the uplands (600-3500 mts) of Nuclear Central America, where they occur from Southern Mexico (Sierra Madre of extreme South Eastern Oaxaca and Chiapas) through to the highlands of Guatemala and Northern El Salvador to Central and Northern Honduras (Duellman 2001).
The distribution of P. guatemalensis species is very interesting; and the utilization of genetic and biogeographic tools can help us understand a lot about how the genetic diversity has been distributed between populations. Duellman and Campbell (1992) suggested that the sister-group of P. guatemalensis was the Hyla bistincta group, which is restricted to the Mexican highlands northwest of the Isthmus of Tehuantepec. By doing morphology and phylogenetic analysis they also found that the actual distribution of P. guatemalensis in Guatemala is associated with geological events in the highlands of Nuclear Central America. The division of the highlands of the North Eastern part of the country occurred after the formation of the Motagua and Polochic faults, which are the result of the movement and interaction of the North American, Caribbean and Cocos plates (Plafker 1976 in Duellman and Campbell 1992).
Figure 1 Map of the endemism zones proposed by Schuster et al. (2000).
18 These two main highlands in Guatemala are separated by the sub-humid corridor proposed by Stuart (1953). The forest at the top of these volcanoes are isolated from one another, making them important endemic areas, especially so for Passalid beetles (Schuster et al. 2000; Schuster & Cano 2005). Utilizing Passalid beetles as indicators Schuster et al. (2000) suggested seven endemism areas for Nuclear Central America (Figure 1).
Figure 2 Map of the distribution of P. guatemalensis in Guatemala (Data shapefile obtained from IUCN 2010).
19 Plectrohyla guatemalensis (figure. 3) is a stream breeding species that inhabits cloud forests and premontane forest (Santos-Barrera & Canseco-Márquez 2010). The distribution is mostly on the Pacific Volcanic Chain (figure 2). The individuals have well-developed tubercles on the dorsal surface of the head, body and limbs. Males have an enlarged prepolex with two curves spines (bifid prepolex) and lack of vocal slits. It can be distributed from 950 to 2600 meters, occurring in Sierra Madre from South Eastern Chiapas, Mexico, eastward through the central and southeastern highlands of Guatemala to North Western El Salvador, and Central Honduras (Duellman 2001).
Throughout the years the populations have suffered declines, the IUCN classifies it as Critically Endangered (CR). In Guatemala there is no major study of this species, but specimens with chytridiomicosis have been found in Laj Chimel, Quiche and Biosphere Reserve Trifinio, Chiquimula (Zamora unpublished data). In Honduras they have found some tadpoles with signs of chytridiomicosis (Santos-Barrera & Canseco- Márquez 2010).
Figure 3 Plectrohyla guatemalensis collected in the forests of Laj Chimel, Quiche.
20 Aims of the project
The project aims to assess the levels of genetic diversity and get preliminary information about the status of P. guatemalensis populations in Guatemala in order to help to conserve the species, along with the services it provides. Also the development of microsatellite primers using both Next Generation Sequencing (NGS) techniques and conventional cloning methods was. Specially since there are no microsatellites primers for amphibians developed from NGS data.
The developed microsatellite markers will help to get more information about the different populations of P. guatemalensis in neighbour countries, the diversity levels, the factors that influence this diversity and the impact it can have on ecosystem services. The project also aimed to raise conscience about the importance of the genetic aspect in conservation, especially in tropical regions where amphibians are very diverse.
21 Methods
Field survey
Plectrohyla guatemalensis surveys were done between February and April 2013, in seven departments of Guatemala (Chiquimula, Guatemala, Huehuetenango, Jalapa, Quiché, San Marcos and Suchitepéquez (Figure 1). Unfortunately frogs from the P. guatemalensis genera were found only in three of these departments, Laj Chimel Community Forest, Quiché; Trifinio Biosfere Reserve, Chiquimula and Tatasiriré Ecotursitic Park, Jalapa (Figure 4). The project was done under the permits of CONAP (Consejo Nacional de Areas Protegidas-National Council of Protected Areas): investigator license number 00101-B, collection license number Serie A 000355 and the samples were imported to Manchester University under the exportation license number 005775 (Appendix figures 8, 9 and 10 ).
Laj Chimel is located in the Northern chain of the Sierra Madre Mountains, in the department of Quiché. These mountains date from the Cretaceous period and are part of one of the oldest terrain in Guatemala. Trifinio and Tatasiriré are located on the east of Guatemala, the mountains and volcanoes on that side date from around the Pleistocene (pers. comm. Dr. Jack Shuster). Laj Chimel is a protected community forest, divided into pristine forest and plots of land. Rights to use the land can be purchased by people from the community, usually for use in a sustainable way (e.g. shadow coffee plantations). The P. guatemalensis samples came from Cuatro Chorros river, this is located deep inside the forest where there is no much human activities; there are no car roads connecting directly from the communities, to reach it, it is necessary a 5 hour walk into the forest from Laj Chimel village, which at the same time is located 2 hours by car drive from the closest township, Uspantán (pers. comm. Philip Tanimoto and Elias Barrera).
Trifinio Biosfere Reserve is a Tri-national initiative, between El Salvador, Honduras and Guatemala to protect the endemic forest shared by the three countries. This is one of the most important protected areas of Guatemala, and like Laj Chimel is part of the seven endemic areas for the region proposed by Shuster et al. (2000), and is important for scientific research. This reserve is divided into pristine zone and multiple use zones, in
22
which shadow coffee is cultivated. The communities and villages are closer to the border of the forest and there are a series of trails connecting the different villages, also a car road that connects Plan de la Arada Village (where the biological station is located) to Esquipulas, a big township of Guatemala.
Tatasiriré Waterfalls Eco-touristic Park is located in the eastern part of the country, in Montaña Miramundo. This park is well known for its waterfalls and the ecological activities that tourist can enjoy. Inside the park there is no agriculture or cattle raising activities. However the adjacent forest has been destroyed and there are several settlements surrounding this and other natural parks in the area, a main highway that connects to Guatemala City runs alongside. The destruction of the habitat makes the natural parks in Miramundo isolated patches of forest where it is assumed that gene flow between populations will be difficult.
The surveys were done between 19.00 and 23.00hrs, using the Visual Encounter Survey (VES) method and because of the time of the year, most of the specimens captured were tadpoles. Every individual collected was put in a plastic bag with a serial number. The data recorded included: the type of the microhabitat where it was collected, the activity and the hour it was taken. The individuals were collected using disposable latex gloves; also the field equipment and boots were cleaned using a chlorine solution to avoid the spread of the chytrid fungus.
In the case of most adults, a sample of saliva was taken using a special rayon tip swab. When it was necessary to sacrifice the specimen, dental anesthesia was used in order to euthanize the individuals, then a sample of the liver was taken; for the tadpoles a sample of tail muscle was included. The tissues were stored in ethanol 95% (see permit numbers above). The individuals were identified using amphibian field guides for the area (Campbell & Duellman 1992; McCranie and Wilson 2002; Köhler 2011).
The individuals collected and a copy of the tissues obtained were deposited at the Biological Reference Collection at Universidad del Valle de Guatemala (UVG).
23
Figure 4 Map of the localities where P. guatemalensis surveys were done. Red triangles show localities were the species was found. 1. Huehuetenango; 2. San Marcos; 3. Quiché; 4. Suchitepéquez; 5. Guatemala; 6. Jalapa; 7. Chiquimula. The maps were produced using Diva Gis 7.5 (Hijmans et al. 2012) spatial data.
24
Table 1 Localities where P. guatemalensis surveys were conducted
Elevation Site Township Department Longitude Latitude (m.a.s.l) Tonishaje River Tajumulco San Marcos -91.94748 15.04633 1779 Tocapote Forest Sibinal San Marcos -92.05811 15.13798 2547 Canjula Forest Sibinal San Marcos -92.05483 15.13303 2685 Coffee plantations Finca la Bolsa La Libertad Huehuetenango -91.94255 15.58241 1270 River Finca La Bolsa La Libertad Huehuetenango -91.94255 15.58241 1270 River Finca El Injerto La Libertad Huehuetenango -91.94386 15.55688 1510 La Cumbre, Finca El Injerto La Libertad Huehuetenango -91.94255 15.58241 2198 Cuatro Chorros River, Laj Chimel Forest Uspantán Quiché -90.79208 15.5122 1700 Natural Private Reserve Los Tarrales Patulul Suchitepéquez -91.15000 14.53583 1029 Monte Cristo, Biosphere 14.51600 Reserve El Trifinio Esquipulas Chiquimula -89.3848 1700 Tatasirire Waterfalls Park Jalapa Jalapa -90.09546 14.56607 2100 Km 30 Highway to El Salvador Fraijanes Guatemala -90.46404 14.502518 1600
DNA extraction
For the DNA extraction the Relia Prep gDNA Kit (Promega, UK) was used with the first samples, but when the DNA was quantified the concentration was considered to be low. Therefore a modification of the Zolan & Pukkila (1986) phenol-chloroform protocol was also used. This resulted in a higher yield of DNA so was used for the remaining samples. To each sample 500 μl of buffer TENS (Tris pH8; 5M NaCl; 0.5N EDTA pH8; 10% SDS) and 10 μl of Proteinase K were added and then they were incubated at 65°C for four hours.
25
After this, the sample was transferred to a Phase-lock Heavy tube (5Prime), if there was a swab sample, it was discarded and 1 volume of phenol:chloroform:isoamyl alcohol was added to the same tube. The samples were centrifuged for ten minutes and the upper layer was transferred to a clean 1.5ml tube. One volume of cold isopropanol was added and the samples were stored at -20°C overnight. The samples were centrifuged at max speed for ten minutes and the supernatent was discarded. The pellet was washed with 250ul of 70% ethanol and centrifuged for ten minutes after which the ethanol was discarded. The pellet was left to dry at room temperature and the DNA was resuspended in 50ul of TE (1M Tris; 0.5M EDTA) buffer.
Microsatellite Primer development
The primers used in this study were developed using two different approaches. The first set was developed by the conventional cloning method at the NERC Biolmolecular Analysis Facility at Sheffield University (SHF) by Deborah Dawson and Gavin Horsburgh.
The second set was developed using Illumina® MiSeq Next Generation Sequencing (NGS) at Manchester University. In order to obtain this, a sample of P. guatemalensis (OAZ 92, Cuatro Chorros River, Laj Chimel, Quiché) DNA was sent to the Manchester University NGS Sequencing facility.
The Raw Illumina® reads were analyzed using the Pearl script PAL-FINDER (Castoe et al. 2012b) to find Possible Amplifiable Loci (PALs). The algorithm searches for microsatellites in the sequence data according to the parameters the investigator introduces to the script (length and number of tandem repeats for the microsatellites, length of the primers, tm range). For the frog sequences the program was configured to search for tandem repeats from 2mer to 6mer, the minimum number of repeats was set to 6 for 2mer; 4 for 3mer; 3 for 4 mer; 3 for 5 mer and 3 for 6 mer. The Tm was set to 58 to 65, to ensure optimal and specific binding of the primers. This gave back an excel file with millions of PALs, which were then organized according to the n-mer repetition from the smallest to the
26
largest. These were filtered to use only a pair of primers that occurr only once in the data set. Primers for loci having repetitions of 2, 3 and 4mer were selected, since are the most utilized. The smallest loci chosen was composed of 60bp and the largest 336bp, though this was the only one with that size, since the second larger was composed of 165bp. All the chosen primers were analyzed in Primer3 (Koressaar and Remm 2007; Untergrasser et al. 2012) to select pairs of primers with similar Tm values to be able to designed multiplex PCRs and to avoid those with a high chance of secondary structure to ensure the hability of the primers to hybridize to the sequence.
DNA amplification
A set of 21 NGS primers and 16 SHF primers were selected and tested (The complete list of primer sequences is listed in Table 5 in appendices). The primers were diluted in TE buffer to a final concentration of 0.2uM. A PCR master mix was made using the QIAGEN multiplex PCR kit, with the SHF laboratory modification (using only 1 μl of the kit´s master mix and 1 μl of the primer mix). DNA (50ng/μl) volumes of 1 μl were placed on a 96-well PCR plate and dried for 30 minutes at room temperature. Then 2 μl of the master mix was added to each sample and 15 μl of mineral oil to avoid the evaporation of the PCR reaction mix. The plates were sealed and centrifuged.
The PCR program was 95°C for 15 minutes followed by 35 cycles of 94°C for 30 s; 56°C for 90s and 72°C for 60 s, followed by a 30 minutes extension at 60°C, before holding the temperature at 4°C. In the case of the NGS primers the Tm was modified to 58°C. The PCRs were performed on an ABI Bio-systems thermocycler.
The PCR products were visualized in agarose gels 1-3%. Twenty-eight successful primers were selected to design multiplex PCRs (Table 2) this was done using the Multiple Manager Software (Holleley and Gerts 2009). 6-FAM (blue) and the HEX (green) were the dyes chosen to label the primers.
27
Microsatellite Genotyping
For preparing the samples for the genotype process the PCR products were diluted with
30μl of ddH2O. In a new plate 45 μl of ddH2O was added to each well and 5μl of the diluted PCR product was mixed into it. On a new skirt plate 1μl of the new mix was put on each well. The Applied Biosystems GeneScan TM –500 LIZ® was included as an internal size standard, which can size DNA fragments from 35 to 500 bp. In a fumehood the 500 LIZ® size standard was mixed with formamide; 4μl of the standard was mixed for every ml of formamide. Then 9μl of 500 LIZ®-formamide mix was added to the skirt plate. The plate was covered and centrifuged. Genotyping was conducted on an ABI 3730 sequencer at the University of Manchester. Each sample was run twice for each marker.
Data Analysis
The software STRand 2.4.59 (Toonen & Hughes 2001; Hughes 2006) was used to score the loci. This program displays the samples’ electropherogram profiles and shows the peaks and alleles called for each locus. To mark a peak, this had to be inside the allele range for each locus, and they show the size of the allele for each different loci. The samples were visualized one by one to ensure correct genotype scoring.
The population was tested for null alleles using the program FreeNA (Chapuis and Estoup 2007), that uses the EM algorithm developed by Dempster et al.1977. This classifies the null allele frequency in three classes negligible (r < 0.05), moderate (0.05 ≤ r < 0.20), or large (r ≥ 0.20) (Chapuis and Estoup 2007). The Allele frequency and Nei’s genetic distance (Nei 1972) was obtained using GenALEX 6.5 (Peakall & Smouse 2012). The allelic richness analysis was done in FSTAT 2.9.3.2 (Goudet 2002). For calculating the F- statistics, heterozygosis levels, genetic differentiation (Pairwise FST) and the Analysis of Molecular Variance (AMOVA) the software ARLEQUIN v3.5.1.2 (Excoffier & Lischer, 2010) was used. The Hardy Weinberg equilibrium using the Guo & Thompson (1992) method was calculated in GENEPOP version 4.2 (Raymond & Rousset 1995; Rousset 2008).
28
To determine the number of distinct populations present the program STRUCTURE 2.3.4 (Pritchard et al.2000; Falush et al. 2007) was used. To analyze the data STRUCTURE uses a Bayesian clustering algorithm to determine the most likely number of populations (k=clusters), and then it assigns each individual to a cluster, based on their genetically similarity. For running the model a burn-in period of 100,000 steps (number of times the model runs before the parameters settle to have solid data) followed by 1 million MCMC repetitions to assure the best fit of the models. The number of clusters (k) was set from 1 to 10, and 20 iterations runs were done at each value of k.
The output was analyzed using the online tool STRUCTURE HARVERSTER (Earl & vonHoldt 2012). This program implements the Evanno method (Evanno et al. 2005), which is an algorithm that allows the identification of genetically similar individuals and the detection of the most likely number of k for the data. The STRUCTURE HARVESTER output was then run in CLUMPP 1.1.2 (Jakobsson & Rosenberg 2007), which aligns the cluster assignment of the replicate analysis. To do this independent runs the data were done using the Full search algorithm. Since the data was not extensive the Full search could be used without compromising computational power. To obtain the final graph the output files obtained from CLUMPP were loaded and ran in DISTRUCT 1.1 (Rosenberg 2004). This shows a graph that assigns a different colour to the clusters, each individual is represented by a bar segmented in different colours depending on the individuals membership to each cluster or population.
29
Table 2 Multiplex PCR tested for the P. guatemalensis samples
Multiplex Primer name Forward sequence Reverse sequence TF04_D05 * [6-FAM]GTTTGATTGGTCACCTTTCTCC TGGCAACATCAAATCCTATCAC A TF04_C11* [HEX]CGTGGACCTTCTATGTTGCTG GGCATATGTGTACCCACTACCAG TF08_E11 [6-FAM]CAGGGACACAGGAAATGGTC TTCATGTGAAGCACCTGAGTG B+ TF08_F10 [HEX]ATTTAGAATTGACCATAACAGAAAGG CTTCTGGCCACTGGTAGATAAG TF08_D02 [6-FAM]GATCCGTTCATGAGATGCAG ACTGTCACATGAGATTGCTTCTG C TF08_C10* [HEX]CAGGTTCCAGCCACAAGAAG CACCACTCAGGCTGTGTAACC TF08_D11* [6-FAM]AGCTGCTTGTAAAGAGATAACTTGC GAGACCTGGGCATTATGGAG D TF08_B07 [HEX]TGATGTCACCACCTCTGACC CACCTCATTCTTCCCTAAATGC TF08_D03 [6-FAM]TGCCGAGAACACTGAGTAATG AAACAATATGTTTTAATAGTTCGGACA E TF08_B02* [HEX]ACAGACAGGCAGTTGGATTTC AAACTCTAGTTCAGCCAAACAGC TF08_E07 [6-FAM]CCAAATACCAGCTCTTCTAGACC GAAACTAAATTTAATGAGGAGGATACC F TF08_C07 [HEX]GATCAATAAGGGTTCAAGTATGTGC CAGCCAGTACTAGATGACAGTTGG TF04_C09 [6-FAM]GACACTGAGCTTTCCTGGTGT TTTCTATGGAACAGGAAGTGTCAT G+ TF08_D05 [HEX]AGGAATTAAAGGCCGTACCC AATAGTGGCCCGTGTTGG TF08_B04 * [6-FAM]AACAGGTAAGGAATTTGTAAGACAGG AGCACTTCGAGAATGCCAAG H TF08_B10 [HEX]GTCATGAAGAATGAAGGCTGTTAC GGTCAGAACATGAGCATTGTG 4TR III [6-FAM]AACACACACCTCCCTCCTCC TATGTATGCAGCAGAGCGGG I 4TR VI [HEX]TCTATGTATCATCCATCCTGCC CAGACATACAGGTGTGTGATGG 4TR II [6-FAM]CACCTATCTGGGAGTGCTGC CAGGTATCTCCGGCTCTTCC J 3TR VII [HEX]GATGGATAAGAAGGAGGAGGG GCTATCAGGGAAATTCATGGC 2TR III * [6-FAM]CCACGGATCAAACACACCC CGGACGGTGTGAACTTAGCC K 2TR VII * [HEX]CACATGCACACAATGCCTCC GGGCTTTGGTGTGTGGGG 3TR IV [6-FAM]CACATCACAGGACACGGACC AGGCGGATATACCTGTCCCG L 4TR IV [HEX]ATACAGAGCTCCGAGACCACG CGTGATCCATCCCTCCCC 3TR III [6-FAM]ATCTCAGTGGAGGCACACCC GGAAGAGCAGGAGCAAGACC M 3TR VI [HEX]TGAGGGTAGGATGATGATGG CACAATGATGACCAAACACG 4TR I [6-FAM]TTCCTACTTGCACGACTGCC GCTCCAAGTTGTTGGCTTCC N 3TR V [HEX]TACGGAAAACTCTGTCCCCG CAGAGGTGTCCGGAGAAGG Multiplex A to H comprises primers developed at Sheffield University; Multiplex I to L comprises primers developed at the University of Manchester. +These loci were not genotyped, since they did not showed clear bands in the agarose gels. * Primers used in the last set.
30 Results
Field Surveys and population status
Plectrohyla guatemalensis was found in only 3 of the 7 localities sampled (Table 1, Figure 4): Community Forest of Laj Chimel in Quiché; Trifinio Biosfere Reserve in Chiquimula and Eco-touristic Park Tatasiriré WaterFalls in Jalapa (Figure 4). The first two sites are considered to be within the important endemic zones of the Nuclear Central America region (Shuster et al. 2000). In terms of herpetology the three sites are of great importance (Stuart 1963; Parra-Olea et al. 2004; Rovito et al. 2009; Ariano-Sanchez & Torres 2010; Campbell et al. 2010). A total of 94 samples were used in this project; 24 samples came from Laj Chimel, 37 from Trifinio and 33 from Tatasirire.
Microsatellite development
The next generation sequencing returned over 13 million (6,632,541 X 2, for the forward and reverse sequence) pair end reads of 250b. After running the NGS fasta file through the Pal Finder Script a total of 1.1 million reads with microsatellites were obtained. After filtering the file to use the primers that were found only once in the data set, the result gave over 16 thousand possible primer pairs. From this data set, primers with 2, 3 and 4 tandem repeats were chosen randomly and then tested using Primer3. Primers with a low chance for secondary structure and similar melting temperature were chosen. In total 21 NGS primers, which were divided into 8 primers with 2 tandem repeats, 6 with 3 tandem repeats and 5 with 4 tandem repeats were tested.
The final set comprised a total of 37 microsatellite primers, 16 developed by SHF and 21 from NGS (complete list in Table 5 in appendix). After testing them, only 28 amplified by conventional PCR and show good quality bands in the agarose gels, these were used to design the multiplex PCRs. In total 24 loci were genotyped, 12 from SHF and 12 from NGS (Table 2). While visualizing the results in STRand for the 24 loci genotyped, loci that presented good quality peaks inside the expected range size, were scored. The loci that
31 amplified only for a few individuals at each population, loci with peaks with odd shapes that made them difficult to score reliably or that were completely outside the size range were not scored. Also loci from both data sets (six for the NGS and four for the SHF) presented more than two peaks per sample, these were also not scored.
The nine evaluated microsatellites were all found to be polymorphic, having 3-25 alleles per locus. Locus TF04_D05 had the highest number of alleles (25). The average allelic richness mean was similar between localities, across all populations it ranged from 2.15 to 14.25 for TF08_B04 and TF04_D05, respectively.
The lowest number of alleles was found for microsatellite TF08_B04 with only three (table 3). The allelic size distribution was not continuous for some cases, which would that suggest that the mutation pattern is underpinned by the infinite allele mutation process (Figures 5 a-i), which means that each mutation is going to create a new allele. For this reason FST values were obtained instead of RST which assumes a one step mutation process (Slatkin 1995).
The FreeNA software results for the nine loci chosen after the STRand showed that only loci TF08_B02 and 2TR III at Laj Chimel and TF08_B04 at Tatasiriré had a moderate frequency, the rest of the loci showed a negligible frequency (Table 3). Since the loci with moderate frequency performed well for the other populations and the values are not high they were kept in the study.
For realizing the population genetics analysis only eight loci (Table 2) were selected. Selection was based on the ones that amplified in most of the samples of each population, the ones that did not have a high frequency of null alleles and also showed a good pattern of class size. The locus TF08_B04 was eliminated from the final set, since it had two different class sizes, most of the individuals were heterozygotes having one allele with a small class size and other with a large size (Figure 5g), which can indicate that the primers were amplifying at two different loci within the genome. Also it showed very high negative values for the FIS index.
32 Table 3 Sample size (N) Number of different alleles (Na), and null allele frequency for the three populations analyzed
Population Locus N Na Null Allele frequency * TF04_D05 20 10 0 16 12 0.00258 TF04_C11 TF08_C10 13 13 0.0237 Laj Chimel TF08_D11 19 8 0.00001
TF08_B07 21 3 0
17 8 0.1301 TF08_B02 TF08_B04 24 3 0 2TR III 24 6 0.10555 2TR VII 16 4 0 TF04_D05 37 17 0 31 9 0.0241 TF04_C11 TF08_C10 37 6 0 Trifinio TF08_D11 22 9 0
TF08_B07 16 2 0
19 8 0 TF08_B02 TF08_B04 37 2 0.09184 2TR III 36 3 0 2TR VII 31 4 0 TF04_D05 24 12 0.00176 29 6 0.00175 TF04_C11 TF08_C10 27 4 0 Tatasiriré TF08_D11 17 8 0.00001
TF08_B07 17 6 0
14 7 0.00041 TF08_B02 TF08_B04 24 2 0 2TR III 22 12 0 2TR VII 27 11 0 *Chapuis & Estoup (2007) classified the null allele frequency into three classes negligible (r < 0.05), moderate (0.05 ≤ r < 0.20), or large (r ≥ 0.20).
33 0,600 TF04_D05
0,500
0,400
0,300 Frequency 0,200 Laj Chimel Trifinio 0,100 Tatasirire 0,000 244 248 252 256 260 264 268 272 276 280 284 288 292 298 302 306 310 TF04_D05 Class size a.
TF04_C11 0,800 0,700 0,600
0,500 0,400 Laj Chimel
Frequency 0,300 Trifinio 0,200 Tatasirire 0,100
0,000
258 272 288 302 250 252 254 256 260 262 264 266 268 270 274 276 278 280 282 284 286 290 292 294 296 298 300 TF04_C11 Class size b.
34 TF08_C10 0,900 0,800 0,700
0,600 0,500 0,400 Laj Chimel Frequency 0,300 Trifinio 0,200 Tatasirire 0,100 0,000 140 144 148 152 156 160 164 168 172 176 180 184 188 192 196 200 210 214 222 232 TF08_C10 Class size c.
TF08_D11 0,450 0,400 0,350
0,300 0,250 0,200 Laj Chimel Frequency 0,150 Trifinio 0,100 Tatasirire 0,050 0,000 188 190 192 194 196 198 200 202 204 206 208 210 212 214 216 TF08_D11 Class size d.
35 TF08_B07 0,700
0,600
0,500
0,400
0,300 Laj Chimel Frequency Trifinio 0,200 Tatasirire 0,100
0,000 92 98 100 102 104 106 TF08_B07 Class size e.
TF08_B02 0,700
0,600
0,500
0,400
0,300 Laj Chimel Frequency Trifinio 0,200 Tatasirire 0,100
0,000 102104106108110112114116118120122124126128130132134136138140142144146 TF08_B02 Class size f.
36 TF08_B04 0,700 0,600
0,500 0,400 0,300 Laj Chimel Frequency 0,200 Trifinio 0,100 Tatasirire
0,000
92 96
152 160 168 100 104 108 112 116 120 124 128 132 136 140 144 148 156 164 172 176 TF08_B04 Class size g.
2TR III 1,200
1,000
0,800
0,600 Laj Chimel
Frequency 0,400 Trifinio Tatasirire 0,200
0,000 124 126 128 130 132 134 136 138 140 142 144 146 148 150 152 154 156 2TR III Class size h.
37 2TR VII 0,800 0,700 0,600
0,500 0,400 Laj Chimel
Frequency 0,300 Trifinio 0,200 Tatasirire 0,100 0,000 114116118120122124126128130132134136138140142144146148150152154156 2TR VII Class size i.
Figure 5 Allele frequency distribution for each locus. a. TF04_D05; b. TF04_11; c. TF08_C10; d. TF08_D11; e. TF08_B07; f. TF08_B02; g. TF08_B04; h. 2TR III; i.2TR VII.
Figure 6 shows that Tatasiriré had the greatest diversity regarding the number of different alleles per maker with a mean of 8.25 alleles, follow by Laj Chimel and Trifinio with 8 and 7.25 respectively. Tatasiriré was the place with highest number of different alleles and highest number of private alleles. Trifinio had lower levels than the other two populations, but only the number of private alleles for Trifinio was significantly different.
38 10,0 0,9 9,0 0,8 Na
8,0 0,7
7,0 Na Freq. >= 5% 0,6 6,0 0,5 Ne 5,0 0,4 No. Private Alleles 4,0
0,3 Heterozygosity
3,0 He Number ofalleles Number 2,0 0,2 Ho 1,0 0,1 0,0 0,0 Laj Chimel Trifinio Tatasirire Populations
Figure 6. Allelic patterns across the three populations. Na = No. of different alleles. Na Freq >= 5% = No. of different alleles with a frequency >= 5%. Ne = No. of effective alleles. No. private alleles = No. of alleles unique to a single population. He = expected heterozygosity. H0 = observed heterozygosity
Population genetics of Plectrohyla guatemalensis
The results for the Hardy Weinberg Equilibrium (HWE) show significant deviation at many loci across the three populations (Table 4). Null alleles, which are common in population genetics studies, result in positive FIS values. In contrast here in the majority of cases the deviation from HWE results in negative FIS values which indicates that the observed heterozygosity (H0) was higher than the expected heterozygosity (He).
The FIS values occurred for many loci across the three populations (Table 4). These negative values can indicate that the population is out-breeding. Studies like Funk et al. (2005) found that the negative values can be caused if the samples come mainly from tadpoles. However some loci presented high positive values, this could indicate small-scale substructure within the populations. These different values could indicate that negative FIS values in some loci could be attributed to the possibility that it is being amplified more than once within the genome.
39 Table 4 Observed heterozygosity (H0), expected heterozygosity (He), Inbreeding index (FIS = (Mean HE - Mean H0) / Mean He), P-value for HWE and allelic richness for the three populations analyzed
Population Locus H0 He FIS Allelic richness 0.750 0.733 8.68 TF04_D05 -0.023 0.688 0.813 10.62 TF04_C11 0.158* 0.846 0.917 13.00 TF08_C10 0.080 0.947 0.791 7.09 Laj Chimel TF08_D11 -0.204* 0.619 0.598 3.00 TF08_B07 -0.035*
0.765 0.647 6.59 TF08_B02 -0.188* 0.750 0.504 2.54 TF08_B04 -0.502* 0.208 0.370 4.64 2TR III 0.441* 0.688 0.579 3.94 2TR VII -0.195* 0.892 0.913 12.39 TF04_D05 0.023 0.710 0.771 6.65 TF04_C11 0.080 0.432 0.486 4.70 TF08_C10 0.112 0.818 0.789 7.71 Trifinio TF08_D11 -0.038* 0.750 0.484 2.00 TF08_B07 -0.578*
0.368 0.620 6.99 TF08_B02 0.412* 0.676 0.505 2.00 TF08_B04 -0.343 0.028 0.082 1.96 2TR III 0.663* 0.452 0.405 2.84 2TR VII -0.118 0.958 0.905 10.33 TF04_D05 -0.060 0.517 0.486 4.18 TF04_C11 -0.064 0.370 0.344 2.96 TF08_C10 -0.078* 0.765 0.761 6.82 Tatasiriré TF08_D11 -0.005* 0.941 0.738 5.71 TF08_B07 -0.286*
0.714 0.799 6.79 TF08_B02 0.109* 0.625 0.467 2.00 TF08_B04 -0.347 1.000 0.889 9.98 2TR III -0.128* 0.852 0.695 8.14 2TR VII -0.231* *Significant deviation from HWE (p<0.05)
The Nei unbiased genetic distance results show the highest difference between Laj Chimel and Trifinio (1.785), followed by Trifinio and Tatasiriré (1.248) and at last Laj Chimel and Tatasiriré (1.001). The average gene diversity was obtained from ARLEQUIN; Laj Chimel obtained the higher value, 0.378. Trifinio and Tatasiriré obtained 0.353 and 0.308, respectively. Laj Chimel and Trifinio were the populations that presented higher genetic diversity, which can be the reason why the highest genetic distance is seen between these two populations.
40 The analysis of FST pairwise differences gave a result of 0.342 between Laj Chimel and Trifinio; 0.224 between Laj Chimel and Tatasiriré and 0.345 between Trifinio and
Tatasiriré (p-value was significant in all the cases). The overall FST was 0.302. This shows an overall high level of population differentiation.
The AMOVA showed that 69.79% of the variation is within populations and the distribution of genetic diversity among populations was responsible for 30.21% of the variance. This pattern can be seen in all loci, except in 2TR III, in which the variation percentage is higher for the variation among populations (Table 8 in appendix).
After running the model in STRUCTURE and collating the results in STRUCTURE HARVESTER to determine the number of clusters that best fit the data set, the results showed that k=3 is the more suitable number in this case. The graph obtained in DISTRUCT (Figure 7) shows three separated populations (k=3), Laj Chimel= pink; Trifinio= purple and Tatasiriré= green. Each individual is represented by a colour segmented bar which depends in their membership to one of the cluster. In the Laj Chimel cluster two individuals (last bars in the pink cluster) showed probability of membership to the other two clusters, while in the Tatasiriré population one individual showed a higher probability of membership to the Trifinio population (purple bar on the green cluster). This could suggest that even when the populations have been apart and there is no apparent genetic exchange between them, there are still some inviduals whit genetic similarities.
Figure 7. STRUCTURE bar plots for the whole data set (k=3). 1: Laj Chimel (pink); 2: Trifinio (purple); 3: Tatasiriré (green). The graph shows three separated populations, however there is some of genetic similarities between some individuals across populations.
41 Discussion
Population status of Plectrohyla guatemalensis
Among the factors contributing to amphibian declines in Guatemala are habitat loss, pollution, change in land use, accumulation of agricultural residues and infectious diseases (Mendelson III et al. 2004; Rovito et al. 2009). P. guatemalensis used to be distributed all along the mountains of Guatemala but nowadays it is harder to find the species (Santos- Barrera and Canseco-Márquez 2010). In 2004 it was catalogued as critically endangered by the IUCN red list who believed that the decline was mainly due to the chytrid fungus, Batrachochytrium dendrobatidis (Bd) (Santos-Barrera and Canseco-Márquez 2010).
The localities where P. guatemalensis was found in this study have a great deal of biological importance due to their high levels of endemism, not only for amphibians, but also for a lot of groups such as arthropods and birds (Schuster et al. 2000; Cóbar & Romero 2006; Eisermann & Avendaño 2007; Eisermann & Avendaño 2009; Ariano-Sanchez & Torres 2010; Campbell et al. 2010; Eisermann & Avendaño 2011; González-Madrid 2011). The surveys for P. guatemalensis were done in tropical mountain cloud forests in different parts of Guatemala.. P. guatemalensis was found in relatively large fragments of forest all of which have some degree of protection e.g. Trifinio is a Biosphere Reserve; Chimel forest is a community protected forest and Tatasirire is a Private Natural Reserve.
Laj Chimel and Trifinio, are the localities with the best degree of protection and it is believed that the chytrid fungus may be involved in the population declines at these sites. Molecular analysis in Laj Chimel and Trifinio has detected the presence of the pathogen fungus in the area (Zamora unpublished data). In addition, whilst identifying tadpole specimens from the three sites, some individuals showed malformations of the mouthparts, a symptom of the disease (Fellers et al. 2001; Mendelson III et al. 2004; Blaustein et al. 2005; Knapp & Morgan 2006; Smith & Weldon 2007), which should not be taken lightly. In both localities the forest is used in a sustainable way e.g. shadow coffee, and it is known that in Laj Chimel a project involving local people is being developed to create awareness of sustainable forest management (pers. comm. P. Tanimoto).
42 It is believed that Tatasiriré is mostly endangered because of the rapid loss of the adjacent forest and therefore the isolation of the populations. There are records for the area, of population declines of the salamander Pseudoeurycea exspectata (Acevedo et al. 2004), endemic to the forests of Monataña Miramundo, where Tatasiriré and other eco-touristic parks are located. Also endemic reptiles of the area are endangered due to habitat loss (Ariano & Torres 2010). In this area no molecular analysis or deep study has been done for the chytrid fungus. During this project tadpoles with malformed mouthparts were found, and computational models show that this area has high suitability for fungus presence (Zamora unpublished data). This has to be carefully analyzed and more tests have to be done before confirming that the chytrid fungus is present in the area.
The factors that could have influenced the localities where P. guatemalensis was not found, can differ between localities. In the case of the eastern part of the country (Huehuetenango and San Marcos –see Table 2 and Figure 1) Rovito et al. (2009) found that salamander declines could be due to habitat destruction and change in land use. In these localities the forest was also used for crop plantations. In the case of Huehuetenango (Finca La Bolsa and Finca El Injerto) the localities are dedicated to plant coffee, consequently the forest has been fragmented. Another important factor could be the pesticides and chemicals used in the plantations. Mendelson III et al. (2004) associated amphibian declines in Sierra de las Minas (in the west of Guatemala) with pesticide pollution. Also in the case of San Marcos previous studies (unpublished data) have found the presence of chytrid fungus in adjacent areas.
While conservation activities take place in the forest in Los Tarrales, part of the land is utilized for coffee and tea plantations, which leaves only patches of forest for the animals to inhabit. The same pattern of forest fragmentation was found while doing surveys in a forest close to the capital city (Km 30 highway to El Salvador), where the forest consists of remaining patches of bigger forests that have been destroyed to build houses and highways.
In the three areas where this frog was found, both habitats and individuals seem to be healthy. P. guatemalensis was found sharing the habitat with other frog species such as P. hartwegii, P. quecchi and Lithobates spp. More attention should be paid to the fact that P. guatemalensis was only found at three of the seven sites surveyed.
43 For this project the sampling took place between February and April, which is not the reproductive season for this species, and that is the reason the individuals found were mainly tadpoles. This could also have contributed to the fact the species was not found in many localities; it could be the case that within these forests the breeding season is later meaning there were few individuals to be found. Although careful surveys for both tadpoles and adults were made, there is still the need to do more surveys before it can be stated that the species no longer exists at the localities were it was not found. In conjunction with more surveys there needs to be studies of land cover, habitat destruction and diseases to determine whether there is indeed a declining population and if so the most likely cause.
Microsatellite development
The microsatellites developed in this study are the first ones for P. guatemalensis and, as far as is known, the first ones for the Plectrohyla genus. Testing and developing microsatellites with the conventional enrichment methods can be a long process (Zane et al. 2002; Selkoe & Toonen 2006). Using the NGS data and Pal Finder script was a quick and relatively easy way of obtaining thousands of PALs from the pair end reads files. Unfortunately for this study only 21 PALs could be tested, from which only 12 were genotyped. At the end only two primers were used from the NGS data and 6 from the SHF data to do the population genetics analysis.
From the 21 NGS primers tested only 12 were genotyped (57.14%), since they presented positive results after the first PCR. For the last set only two primers were used, which means a success rate of 9.5%. Both of these loci were dinucleotide and comprised AC repeats. From the 16 markers tested from SHF only six where used for the final set, which is a success rate of 37.5%, all loci being dinucleotide. Nevertheless it has to be taken into consideration that for developing microsatellite loci using the conventional cloning techniques a careful screening of several loci has to be done, before being able to test a set. While testing for microsatellites it is always important to test several primers to find a set that works properly, which could not be achieved in this study for cost and time issues. In general the allele diversity and allele range was higher for the loci developed at SHF. For
44 future test it would be advisable to do a deeper screening while developing primers with NGS data.
Something that could have an impact on the genotyping process is that the primers were labeled with the 6-FAM (blue) and HEX (green) dyes, and as internal size standard the 500 LIZ® was used. For the 6-FAM dyes the manufacturer recommends using the size standard 500 ROX®. Nonetheless it did not seem to cause a major issue during the genotyping process, since samples did amplify and good quality peaks within the correct class sizes could be found. It is therefore believed that this did not have an impact on the results of the project.
Researchers have found that developing microsatellite primers for amphibians can be a difficult task due to the big genome, since the larger the genome is, the chance to find target DNA sequences is reduced, this has been demonstrated for salamanders (Garner 2002). It has been recorded that the amphibian genome can be as twice as large as that of the average mammal or bird (Gregory 2001b). This makes the development of amphibian microsatellites a double challenge. Unfortunately there is still no information for P. guatemalensis genome size.
The HWE analysis showed that several loci diverged from equilibrium. The HWE deviation was attributed to the heterozygosity excess seen in at several loci and not to null alleles in the populations. The negative FIS values found in several loci across the three populations, can indicate that the populations are out-breeding. Nonetheless having several loci across the three population negative values, raises the possibility that some primers could have amplified at different sites within the genome, which could have caused the heterozygosity excess.
It is a well-known fact that vertebrate genomes can have large sizes because of the large amount of repetitive sequences or non coding-DNA (Pagel & Johnstone 1992; Gregory 2001a). There is a chance that the loci could be amplified more than once if the genome is comprise of several repetitions. This could be the reason why some loci presented double peaks at very different class sizes during the STRand analysis. For example after scoring
45 peaks for the locus TF08_B04, the results showed alleles with two different and separated size classes (see Figure 6g), and thus the heterozigosity excess and high negative FIS values. This should be investigated carefully by running more samples and testing some of the primers again. Van de Vliet et al. (2009) used a PCR program with touchdown cycles to improve the amplification and avoiding nonspecific binding, this should be an approach to consider while testing these primers.
It is also important to do more studies in the area, extending the study to include more samples from adults. Other studies (Funk et al. 2005) found that in the places where they collected only tadpoles the heterozygosity excess could be due to the chance that the samples represent only the allelic contribution of a few individuals. In this study the negative FIS values were found in the three populations, which make it more likely that the high negative values were due to amplification process.
Using NGS researchers will have access, in a relatively quick and easy way to a large number of possible primers to work with, especially now where there are centres that can sequence a sample in a few days or even hours. A great advantage of obtaining the NGS data is that the information can be useful to develop other markers, like SNPs and for different genomic studies. So the costs of sequencing the genome can be justified by all the different applications the information can be used for (Castoe et al. 2012a). Also the time it took to develop the NGS microsatellites was considerably lower. In the case of P. guatemalensis microsatellites loci, more primers should be tested before the advantages of NGS over conventional methods can be concluded.
46 Preliminary Population Genetics Analysis of Plectrohyla guatemalensis
It has been found that the heterozygosity excess can be attributed to populations declines; a pattern that has been found in amphibians (Beebee & Rowe 2001; Spear et al. 2005). This excess can be due to rapid population declines, which can cause the rapid loss of rare alleles, even in a faster rate than the overall heterozygosity (Beebee 2005).
The overall FST value of 0.302 and the fact that the AMOVA detected that 30.2% of the variation was found among populations indicates high levels of differentiation between populations. The FST pairwise analysis found a greater differentiation between Trifinio and Tatasiriré, followed by Laj Chimel with Trifinio and the least difference was between Laj Chimel and Tatasiriré. This indicates that there is no gene flow between the three populations and that they are isolated from each other. The STRUCTURE model also showed three separated populations, with no gene flow between them. Funk et al. (2005) found similar results, concluding that mountain ridges can cause low dispersal rates, even in species that have the potential to move long distances. This would be interesting to analyze for Plectrohyla in general, especially in the case of some species like P. guatemalensis, which distribution area is considerable large, so potentially they could have high dispersal rates. García-Paris et al. (2000) and Schoville et al. (2011) found similar results of population differentiation in Plethodontidae salamanders and Rana muscosa, respectively. In amphibians substructure within populations can be a common pattern (Shaffer et al. 2000; Palo et al. 2004). In this study there is evidence of small-scale substructure (high positive FIS values for some loci). This can be caused by the fact that males and females mate within a particular area of their habitat; this localized mating can therefore generate genetic substructure within a population.
Nei’s genetic distance showed that Laj Chimel and Trifinio are the most genetically distant sites. These two localities have a great extent of pristine forest; in this project there was no evidence of gene flow between them. However, there is the posibility of gene flow between these populations and other populations, or even with neighbouring forests, especially in
47 the case of Trifinio, where the forest covers area in three countries. This possible exchange with other neighboring populations could be the source for having a greater allelic diversity within each population, which can make the localities more distant in terms of genetic differentiation among them.
The analysis for allelic genetic diversity showed that Tatasiriré Park had the least genetic diversity. Nonetheless it is necessary to do more surveys and obtain more data before determining that Tatasirire diversity is significantly lower. This is the location with the highest levels of human disturbance and the most isolated population. If there is a genetic exchange with the neighboring parks it must be low. Tatasiriré is located in Montaña Miramundo, which comprises forest patches with important herpetological endemism (Campbell et al. 2010 and Ariano-Sanchez & Torres 2010). Sadly during the last years it has been exploited by local communities and species like the salamander Pseudoeurycea exspectata have not been seen in years (Acevedo et al. 2004), and some like the lizard Abronia campbelli remain in small separate fragments of forest (Ariano-Sanchez & Torres 2010). Both Laj Chimel and Trifinio have had better management of the parks and associated areas.
Conservation and the future
In Guatemala studies about amphibians and evolution have been conducted during the last decade (Parra et al. 2004; Rovito et al. 2009), but none of them combines conservation genetics with an area management plan. Significant amounts of land have been purchased in high diversity areas to avoid the destruction and misuse of unique species’ habitat. However, no large-scale investigations have been done in these areas, and most studies just focus on making inventories of the species or trying to find species that have not been seen in a while.
Having knowledge of what species exists in each locality is without doubt of major importance. The problem is that some efforts just stop there and most of the tissues collected remain in museums and reference collections at Universities awaiting future uses. Again these tissues are of major importance (especially in the case of rare species) and
48 reflect the hard work and dedication of years of surveys from researchers (Stuart 1948; Stuart 1963; Campbell 1998; Lee 200; Acevedo 2006; Parra et al. 2004; Rovito et al. 2009; Campbell et al. 2010; Köhler 2011) and undergraduate students (pers. comm Jacobo Conde). The problem is that there are no major initiatives to use them for studying genetic aspects, and there is often over-collection in some areas. Nowadays there is no need to sacrifice all the individuals, especially with the new non-invasive sampling methods (Pidancier et al. 2003; Broquet et al. 2007).
In some part of the tropics, amphibians can comprise the most abundant land vertebrates (Stebbins and Cohen in Whiles et al. 2006), and they are involved with both aquatic and land habitats. Research done in Central America has demonstrated that stream-breeding frogs are one of the most endangered groups and this can have a lot of consequences for the aquatic ecosystems (Whiles et al. 2006). P. guatemalensis is a stream-breeding frog, which can contribute to several services inside the forests; their tadpoles could provide an important service as part of the “bottom-up” effects on the food chain. Since it is recorded that tadpoles can help exposing food resources to other organisms (Ranvestal et al. 2004; Whiles et al. 2006).
The three localities where P. guatemalensis was found harbor many unique endemic species, consequently important genetic resources could be lost if any of these habitats were to be destroyed. Protecting these remaining forests and ensuring they are correctly managed could be important in maintaining biodiversity through the conservation of entire ecosystems. In the future P. guatemalensis could be used as an umbrella species, as protecting this species would require the protection of much of the fauna and flora in the ecosystem. By taking care of the amphibian populations, the entire ecosystem and its associated services can be preserved which would have many positive impacts for humans.
Unfortunately there is still not enough information about the consequences the destruction of amphibian ecosystems could have; nevertheless there is no chance of a good outcome. Amphibian populations are declining fast, therefore it is important to start using tools to make population studies more informative and effective, especially in tropical countries like Guatemala. It is also important to do constant monitoring of the populations in order to detect the changes through time, especially after population declines.
49 The genetic data obtained during this project can be useful to scientists working in the area; especially to design more suitable conservation plans for amphibians. Also this project will help to raise awareness to conservation biologists about the importance of including genetic studies while designing management and conservation plans. Guatemala´s conservation programs can be improved if these initiatives are taken into consideration, not only for amphibians but for different taxa as well.
50 References
Acevedo, M., Wake, D., Rovito, S. & Vasquez C. (2004) Pseudoeurycea exspectata in IUCN. (2013). IUCN Red List of Threatened Species, Version 2013.2, www. www.iucnredlist.org. Acevedo, M. (2006) Anfibios y reptiles de Guatemala una breve síntesis con bibliografía in Cano, E. (ed.) Biodiversidad de Guatemala. Volumen I, Guatemala: Universidad del Valle de Guatemala. AmphibiaWeb. (2013) Data Base Amphibians of Guatemala in: http://amphibiaweb.org/ [consulted 7 January 2013]. Alves, R., Léo, N., Brooks, S. & Albuquerque, U. (2009) Commercialization of animal- derived remedies as complementary medicine in the semi-arid region of Northeastern Brazil, Journal of Ethnopharmacology, vol 124, pp 600–608. Ariano-Sanchez, D. & Torres-Almazan, M. (2010) Rediscovery of Abronia campbelli: sauria anguidae from a pine-oak forest in southeastern Guatemala habitat characterization, natural history, and conservation status, Herpetological Review, vol 41, pp 290-292. Attademo, A.M., Peltzer, P.M. & Lajmanovich, R.C. (2007) Feeding habits of Physalaemus biligonigerus (Anura, Leptodactylidae) from soybean field of Córdoba Province, Argentina, Russian Journal of Herpetology, vol 14, pp 1–6 Balmford, A., Bruner, A. Cooper, P., Costanza, R. Farber, S., Green, R., Jenkins, M. Jefferiss, P. Jessamy, V. Madden, J. Munro, K., Myers, N. Naeem, S. & Paavola, J. (2007) Economic Reasons for Conserving Wild Nature, Science, vol 297, 950-953. Barrat, C., Horsburgh, G., Dawson, D., Gower, D., Wilkinson, M., Loader, S. & Jehle, R. (2011) Characterization of nine micro satellite loci in the caecilian amphibian Boulengerula uluguruensis (Gymnophiona), and their cross-species utility in three congeneric species, Conservation Genetics Resources, vol 4, pp 225-229. Beebee, T.J.C. (2005) Conservation genetics of amphibians, Heredity vol 95, pp 423–427. Blaustein, A.R, Romansic, J.M., Scheessele, E.A., Han, B.A., Pessier, A.P. & Longcore, J.E. (2005) Interespecific variation in susceptibility of frog tadpoles to the pathogenic fungus Batrachochytrium dendrobatidis, Conservation Biology, vol 19, pp 1460–1468 Blaustein, L. & Chase, J.M. (2007) Interactions between mosquito larvae and species that share the same trophic level, Annual Review of Entomology, vol 52, pp 489–507 Beard, K.H., Vogt, K.V. & Kulmatiski, A. (2002) Top-down effects of a terrestrial frog on forest nutrient dynamics, Oecologia, vol 133, pp 583-593. Beard, K.H., Eschtruth, A.K., Vogt, K.A., Vogt, D.J. & Scatena, F.N. (2003) The effects of the frog Eleutherodactylus coqui on invertebrates and ecosystem processes at two scales in the Luquillo experimental forest, Puerto Rico, Journal of Tropical Ecology, vol 19, pp 607- 617. Bellows, T.S. (2001) Restoring population balance through natural enemy introductions, Biological Control, vol 21, pp 199–205. Berger, L., Speare, R., Daszak, P., Green, E., Cunningham, A., Goggin, L., Slocombe, R., Ragan, M., Hyatt, A., McDonald, K., Hines, H., Lips, K., Marantelli, G. & Parkers H. (1998) Chytridiomycosis causes amphibian mortality associated with population declines in the rain forests of Australia and Central America, Proceedings of the National Academy of Science, vol 95, pp 9031-9036.
51 Blaustein, A. & Kiesecker, M. (2002) Complexity in conservation: lessons from the global decline of amphibians populations, Ecology Letters, vol 5, pp 597-608. Brocchi, P. (1877) Description d’un nouveau genre de phaneroglosse hylaeforme (Plectrohyla guatemalensis), Bulletin de la Société Philomathique de Paris. vol 7, pp 92–93. Broquet, T., Berset-Braendli, L., Emaresi, G. & Fumagalli, L. (2007) Buccal swabs efficient and reliable microsatellite genotyping in amphibians, Conservation Genetics, vol 8, pp 509- 511 Calboli, F., Fisher, M., Garner, T.W. and Jehle, R. (2011) The need for jumpstarting amphibian genome projects, Trends in Ecology and Evolution, vol 26, pp 378-379. Campbell, J.A. (1998) Amphibians and Reptiles of Northern Guatemala, the Yucatan, and Belize, Oklahoma: University of Oklahoma Press, Norman, pp 400. Campbell, J.A., Smith, E.N., Streicher, J.W., Acevedo M.E. & Brodie Jr., E.D. (2010) New salamanders (Caudata: Plethodontidae) from Guatemala, with miscellaneous notes on known species, Miscellaneous Publications, Museum of Zoology University of Michigan, vol 200, pp 1-66 Castoe, T.A., Poole, A.W., Gu W., Koning, A.P., Daza, J.M. & Smith, E.N. (2010) Rapid identification of thousands of copperhead snake (Agkistrodon contortrix) microsatellite loci from modest amounts of 454 shotgun genome sequence, Molecular Ecology, vol 10, pp 341– 347. Castoe, T.A., Poole, A.W., Koning, A.P., Jones, K., Tomback, D.F., Oyler-McCance, S.J., Fike, J., Lance, S.L., Streicher, J.W. & Smith, E.N. (2012a) Rapid Microsatellite Identification from Illumina Paired- End Genomic Sequencing in Two Birds and a Snake, PLoS ONE, vol 7(2) Castoe, T., Streicher, A., Meik, J., Ingrasci, JM., Poole M. J., Koning A. W., Campbell, J.A., Parkinson, C.L. & Smith E.N. (2012b) Thousands of microsatellite loci from the venomous coralsnake Micrurus fulvius and variability of select loci across populations and related species, Molecular Ecology Resources, vol 12, pp 1105-1113. Chapuis, M.P. & Estoup, A. (2007) Microsatellite Null Alleles and Estimation of Population Differentiation, Molecular Biology and Evolution, vol 24, pp 621-631. Cheng, T.L., G., Rovito, S.M., Wake, D.B., & Vredenburg, V.T. (2011) Coincident mass extirpation of neotropical amphibians with the emerging infectious fungal pathogen Batrachochytrium dendrobatidis, Proceedings of the National Academy of Sciences of the United States of America, vol 108, pp 9502-9507. Cobar, A.J. & Rivas, J. (2006) Current distribution of the horned guan in San Marcos and Huehuetenango, Guatemala, Annual Review of the World Pheasant Association 2005-2006, vol 23 Collins, J. & Storfer, A. (2003) Global amphibian declines: sorting the hypotheses, Diversity and Distributions, vol 9, pp 89-98. Connelly, S., Pringle, C.M., Whiles, M.R., Lips, K.R., Kilham, S. and Brenes, R. (2011) Do tadpoles affect leaf decomposition in neotropical streams?, Freshwater Biology vol 56, pp 1863–1875. Cuesta-Ríos, E., Valencia-Mazo, J.D. & Jiménez-Ortega, A.M. (2007) Aprovechamiento de los vertebrados terrestres por una comunidad humana en bosque tropicales (Tutunendo, Chocó, Colombia), Revista Institucional Universidad Tecnológica del Chocó: Investigación, Biodiversidad y Desarrollo, vol 26, pp 37-43.
52 Crump, M. (2010) Amphibian diversity and life history in Dood, C.K. (ed.) Amphibian Ecology and Conservation. Techniques in Ecology and Conservation Series, England: University of Oxford. De Groot, R.S., Wilson, M.A. & Boumans, R. (2002) A typology for the classification, description and valuation of ecosystem functions, goods and services, Ecological economics, vol 41, pp 393-408. De la Lande, I.S., O’Brien, P.E., Shearman, D.J., Taylor, P. & Tyler, M.J. (1984) On the possible role of prostaglandin E2 in intestinal stasis in the gastric brooding frog Rheobratrachus silus, The Australian journal of experimental biology and medical science, vol 62, pp 317-323. Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977) Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, vol. 39, pp 1-38. Drechsler, A., Geller, D., Freund, K., Schmeller, D., Künzel, S., Rupp, O., Loyau, A., Denoël, M., Valbuena-Ureña, E. & Steinfartz, S. (2013) What remains from a 454 run: estimation of success rates of microsatellite loci development in selected newt species (Calotriton asper, Lissotriton helveticus, and Triturus cristatus) and comparision with Illumina-based approaches, Ecology and Evolution, vol 3, pp 3947-3957 Duellman, W.E. & Campbell, J.A. (1992) Hylid frogs of the genus Plectrohyla: systematics and phylogenetic relationships, Miscellaneous Publications, Museum of Zoology, University of Michigan, pp 181. Duellman, W.E. (2001) Hylid frogs of Middle America, Volume 2, New York: Society for the Study of Amphibians and Reptiles, pp 1159. Earl, D.A. & vonHoldt, B.M. (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method, Conservation Genetics Resources, vol 4 pp 359-361 Eisermann, K. & Avendaño, C. (2007) Lista comentada de las aves de Guatemala - Annotated checklist of the birds of Guatemala, Barcelona, Spain: Lynx Editions Eisermann, K. & Avendaño, C. (2009) Conservation priority-setting in Guatemala through the identification of important bird areas, Proceedings of the Fourth International Partners in Flight Conference: Tundra to Tropics, pp 315-327 Eisermann, K. & Avendaño, C. (2011) Evaluación de la avifauna del Área Importante para la Conservación de Aves Cerro El Amay (IBA GT006), Quiché, Guatemala, con una evaluación de la importancia de esta IBA para aves migratorias neotropicales, report to U.S. Fish and Wildlife Service, PROEVAL RAXMU Bird Monitoring Program, Cobán, Guatemala. Enalik D., Eldin, E.R., Own, B.R., Arbut, R.E. & Imon, P.H. (2012) Simple sequence repeat (SSR) loci in the plant sciences, American Journal of Botany, vol 99, pp 193–208. Evanno, G., Regnaut, S. & Goudet J. (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study, Molecular Ecology 14 , 2611 - 2620 Excoffier, L. & Lischer, H.E. (2010) Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows, Molecular Ecology Resources, vol 10, pp 564-567. Falush, D., Stephens, M. & J. Pritchard. (2007) Inference of population structure using multilocus genotype data: dominant markers and null alleles, Molecular Ecology Notes vol 7, pp 574-578. Fellers, G.M., Green, D.E., Longcore, J.E. & Gatten, Jr. R.E. (2001) Oral Chytridiomycosis in the Mountain Yellow-Legged Frog (Rana muscosa), Copeia, vol 2001, pp 945-953
53 Freeman, B., Smith, N., Curtis, C., Huckett, L., Mill, J. & Craig, W. (2003) DNA from bucal swabs recruited by mail: Evaluation of storage effects on long-term stability and suitability for multiplex Polymerase Chain Reaction genotyping, Behavioir Genetics, vol 33, pp 67-72 Funk, W.C., Blouinm M.S., Corn, P.S., Maxell, B.A., Pilliod, D.S., Amish, S. & Allendorf, F. (2005) Population structure of Columbia spotted frogs (Rana luteiventris) is strongly affected by the landscape, Molecular Ecology, vol 14, pp 483-496 Funk, W.C., McKay, J.K., Hohenlohe, P.A. & Allendorf, F.W. (2012) Harnessing genomics for delineating conservation units, Trends in Ecology and Evolution, vol 27, pp. 489-496. Gangwal, K. & Lessnick, S.L. (2008) Microsatellites are EWS/FLI response elements, Cell Cycle, vol 7, pp 3127-3131 García-Paris, M., Good, D.A., Parra-Olea, G. & Wake, D.B. (2000) Biodiversity of Costa Rican salamanders: Implications of high levels of genetic differentiation and phylogeographic structure for species formation, Proceedings of the National Academy of Science, vol 97, pp1640-1647. Garner, T.W.J., Gautschi, B., Rothlisberger, S., & Reyer, H.-U. (2000) A set of CA repeat microsatellite markers derived from the pool frog, Rana lessonae, Molecular Ecology, vol 9, pp 2173-2175 Garner, T.W.J. & Tomio, G. (2001) Microsatellites for use in studies of the Italian agile frog, Rana latastei (Boulenger), Conservation Genetics, vol 2, pp 77-80 Garner, T.W.J. (2002) Genome size and microsatellites: the effect of nuclear size on amplification potential, Genome, vol 45, pp 212-215. Garner, T.W.J., Schmidt, B.R., Hoeck, P. & Van Buskirk, J. (2003) Di-and tetranucleotide microsatellite markers for the Alpine newt (Tritirus alpestris): characterization and cross- priming in five congeners, Molecular Ecology Notes, vol 3, pp 186-188. Gratwicke, B., Evans, M.J., Jenkins, P.T., Kusrini, M., Moore, R.D., Sevin, J. & Wildt, D.E. (2009) Is the international frog legs trade a potential vector for deadly amphibian pathogens?, Frontiers in Ecology and the Environment, vol 8, pp 438-442. Gregory, T. (2001a) The bigger the C-Value, the larger the cell: genome size and red blood cell size in vertebrates, Blood Cells, Molecules, and Diseases, vol 27, pp 830-843 Gregory, T. (2001b) Coincidence, coevolution, or causation? DNA content, cell size, and the C-value enigma, Biological Reviews of the Cambridge Philosophical Society, Vol.76, pp 65- 101 Goudet, J. (2002) FSTAT 2.9.3.2 in: http://www2.unil.ch/popgen/softwares/fstat.htm [consulted November 2013]. González Madrid, A. (2011) Actualizando la distribución del pavo de cacho en la región noroeste de Guatemala: El caso de Uspantán, El Quiché, Asociación CON-CIENCIA Universidad del Valle de Guatemala, Consejo Nacional de Áreas Protegidas, Embajadores de las Nubes, Guatemala, Guatemala. Guo, S.W. & Thompson, E.A. (1992) Performing the Exact Test Hardy-Weinberg proportions for multiple alleles, Biometrics, vol 48, pp 361-372. Gutiérrez-Rodríguez, J. & Martínez-Solano, I. (2013) Isolation and characterisation of sixteen polymorphic micro satellite loci in the Western Spadefoot, Pelobates cultripes (Anura: Pelobatidae) via 454 pyrosequencing, Conservation Genetics Resources, vol 5, pp 981-984. Hamblin, M.T., Warburton, M.L. & Buckler, E.S. (2007) Empirical comparison of Simple Sequence Repeats and Single Nucleotide Polymorphisms in assessment of Maize diversity and relatedness, PLoS ONE, vol 2, pp 1-9.
54 Hedrick, P. W. (2001) Conservation genetics: where are we now?, TRENDS in Ecology & Evolution, vol 16, pp 629-636. Hijmans, R., Guarino, L. & Mathur, P. (2012) DIVA-GIS 7.5: Geographic Information System for Biodiversity Research, in http://www.diva-gis.org/. Holleley, C. & P. Geerts. (2009) Multiplex Manager 1.0: a cross-platform computer program that plans and optimizes multiplex PCR. BioTechniques, vol. 46, pp 511–517. Huang, W., Liang, X., and Qu, C. (2013) Development and characterization of novel polymorphic microsatellite loci in Siniperca scherzeri (Steindachner) and Siniperca chuatsi ( Basilewsky ), Molecular Biology Reports, 751-756. Hughes, S. (2006) STRand version 2.4.59 (Nucleic Acid Analysis Software), Regents of the University of California, Davis, in: http://www.vgl.ucdavis.edu/informatics/strand.php [consulted October 2013]. IUCN (International Union for Conservation of Nature), Conservation International & NatureServe. (2010). Plectrohyla guatemalensis, in IUCN. (2013). IUCN Red List of Threatened Species, Version 2013.2 http://maps.iucnredlist.org/map.html?id=55876 Jakobsson, M. & Rosenberg, N.A. (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure, Bioinformatics, vol 23, pp 1801-1806. Jehle, R. & Arntzen, J.W. (2002) Review: Microsatellite markers in amphibian conservation genetics, The Herpetological Journal, vol 12, pp 1-9. Jehle, R. (2010) Herpetology and conservation genetics, Animal conservation¸vol 13, pp 72- 73. Knapp, R.A. & Morgan, J.A. (2006) Tadpole mouthpart depigmentation as an accurate indicator of Chytridiomycosis, an emerging disease of Amphibians, Copeia, vol 2006, pp 188-197. Köhler, G. (2011) Amphibians of Central America, Offenbach: Herpeton Verlag, pp 380. Koressaar, T. & Remm, M. (2007) Enhancements and modifications of primer design program Primer3, Bioinformatics, vol 23, pp 1289-91. Kremen, C. (2005) Managing ecosystem services: what do we need to know about their ecology?, Ecology Letters, vol 8, pp 468-479. Kriger, K. 2009. Why frogs are important?, NGO Save the Frogs!, in: http://www.savethefrogs.com/why-frogs/index.html [consulted December 2012]. Lee, J.C. (2000) A field guide to the amphibians and reptiles of the Maya world: the lowlands of Mexico, Northern Guatemala, and Belize, New York: Cornell University Press, pp 402. Lips, K.R., Burrowes, P.A., Mendelson III, J.R. & Parra-Olea, G. (2005) Amphibian Population Declines in Latin America: A Synthesis, Biotropica, vol 37, pp 222-226. Mazzoni, R., Cunningham, A., Daszak, P., Apolo, A., Perdomo, E. & Speranza, G. (2003) Emerging pathogen of wild amphibians in frogs (Rana catesbeiana) farmed for international trade, Emerging Infectious Diseases, vol 995-998. McCranie, J. & Wilson, L.D. (2002) The Amphibians of Honduras, New York: Society for the Study of Amphibians and Reptiles, pp 625. Mendelson III, J.R., Brodie, Jr. E.D., Malone, J.H., Acevedo, M.E., Baker, Smatresk, N.J. & Campbell, J.A. (2004) Factors associated with the catastrophic decline of a cloudforest frog fauna in Guatemala, Revista de Biologia Tropical, vol 52, pp 991-1000. Millennium Ecosystem Assessment. (2005) Millennium Ecosystem Assessment. Ecosystems and Human Well-being: Biodiversity Synthesis, Washington, DC: World Resources.
55 Mochida, K., Inoue, E., Kurita, K., Hayano, A. & Inoue-Murayama, M. (2012) Development of Microsatellite Markers for Cynops ensicauda (Amphibia:Caudata) by using Next- Generation Sequencing Technology, Current Herpetology, vol 21, pp 117-120. Naidoo, R., Balmford, A., Costanza, R., Fisher, B., Green, R.E., Lehner, B., Malcon, T.R. & Ricketts, T.H. (2008) Global mapping of ecosystem services and conservation priorities, Proceedings of the National Academy of Science, vol 105, pp 9495-9500 Nei, M. (1972) Genetic distance between populations, American Naturalist, vol 106, pp 283- 392. Pagel, M. & Johnstone, R.A. (1992), Variation across species in the size of the nuclear genome supports the junk-DNA explanation for the C-value paradox, Proceedings of the Royal Society, vol 249, pp119-124 Palo, J.U., Schemeller, D.S., Laurila,A., Primmer, C.R., Kuzmin, S.L. & Merilä J. (2004) High degree of population subdivision in a widespread amphibian, Molecular Ecology, vol 13, pp 2631-2644. Parra-Olea, G., García-París, M. & Wake, D. B. (2004). Molecular diversification of salamanders of the tropical American genus Bolitoglossa (Caudata: Plethodontidae) and its evolutionary and biogeographical implications, Biological Journal of the Linnaean Society, vol 81 pp 325–346 Peakall, R. & Smouse, P.E. (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update, Bioinformatics, vol 28, pp 2537-2539. Peltzer, P.M. & Lajmanovich, R.C. (2002) Preliminary studies of food habits of Lysapsus limellus (Anura, Pseudidae) in lentic environments or Paraná river, Argentina, Bulletin de la Société Herpétologique de France, vol 101, pp 53-58. Peltzer, P.M., Attademo, A.M., Lajmanovich, R.C., Junges, C.M., Beltzer, A.H. & Sanchez, L.C. (2010) Trophic dynamics of three sympatric anuran species in a soybean agroecosystem from Santa Fe Province, Argentina, Herpetological Journal, vol 20, 261-269 Pidancier, N., Miquel, C. & Miaud, C. (2003) Buccal swabs as non-destructive tissue sampling method for DNA analysis in amphibians, Herpetological Journal, vol 13, pp 175- 178 Primmer, C.R. & Merilä, J. (2002). A low rate of cross-species microsatellite amplification success in Ranid frogs, Conservation Genetics, vol 3, pp 445-449. Pritchard, J. Stephens, M. & P. Donnelly. (2000) Inference of Population Structure Using Multilocus Genotype Data, Genetics, vol 155, pp 945–959. Raymond M. & Rousset F. (1995) GENEPOP version 1.2: population genetics software for exact tests and ecumenicism, Journal of Heredity, vol 86, pp 248-249 Rosenberg, N.A. (2004) DISTRUCT: a program for the graphical display of population structure, Molecular Ecology Notes, vol 4, pp 137 – 138 Rousset, F. (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux, Molecular Ecology Resources, vol 8, pp 103-106. Rovito, S., Parra-Olea, G., Vásquez, C., Papenfuss, T., & Wake, D. (2009) Dramatic declines in neotropical salamander populations are an important part of the global amphibian crisis, Proceedings of the National Academy of Science, vol 6, pp 231–3236. Rovito, S.M., Wake, D.B., Papenfuss, T.J., Parra-Olea, G., Muñoz, A., & Vasquez-Almazan, C.R. (2012) Species formation and geographic range evolution in a genus of Central American cloud forest salamanders (Dendrotriton), Journal of Biogeography, vol 39, pp 1251-1265.
56 Rowe, G., Beebee, T. J. C. & Burke, T. (2000) A further four polymorphic microsatellite loci in the natterjack toad Bufo calamita, Conservation Genetics, vol 1, pp 371-373. Rowe, G. & Beebee, T.J.C. (2001) Polymerase chain reaction primers for microsatellite loci in the common frog Rana temporaria, Molecular Ecology Notes, vol 1, pp 6-7. Santos-Barrera, G. & Canseco-Márquez, L. (2010) Plectrohyla guatemalensis, in: IUCN. (2012) IUCN Red List of Threatened Species, Version 2012.2, www.iucnredlist.org Schoville, S.D., Tustall, T.S., Vredenburg, V.T., Backin, A.R., Gallegos, E., Wood, D.A. & Fisher, R.N. (2011) Conservation genetics of evolutionary lineages of the endangered mountain yellow-legged frog, Rana muscosa (Amphibia: Ranidae), in southern California, Biological Conservation, vol 144, pp 2031-2040. Schuster, J.C. Cano, E.B. & Cardona, C. (2000) Un método sencillo para priorizar la conservación de los bosques nubosos de Guatemala, usando Passalidae (Coleoptera) como organismos indicadores, Acta Zoológica Mexicana, vol 80, pp 197-209. Schuster, J.C. & Cano, E.B. (2005) La distribuci6n Mesoamericana de Montaña: Síntesis de Passalidae (Col. Scarabaeoidea) para Mesoamérica Nuclear, in: Llorente, J. & Morrone, J.J. (ed.) Regionalizaci6n Biogeografía en Iberoamérica y Tópicos Afines. Primeras Jornadas Biogeográficas de la Red Iberoamericana de Biogeografía y Entomología Sistemática (RIBES XII.I-CYTED). México: Facultad de Ciencias, UNAM. Schuster, J. & Bonis, S. (2008) Biodiversidad de Guatemala en relación con su historia geológica y biogeográfica, in: Guatemala y su Biodiversidad: un enfoque histórico, cultural, biológico y económico, Guatemala: Consejo Nacional de Áreas Protegidas (CONAP). Shaffer, H.B., Fellers, G.M., Magee, A. & Voss, R. (2000) The genetics of amphibians declines: population substructure and molecular differentiation in the Yosemite Toad, Bufo canorus (Anura, Bufonidae) based on single-strand conformation polymorphism analysis (SSCP) and mitochondrial DNA sequence data, Molecular Ecology, vol 9, pp 245-257. Selkoe, K.A. & Toonen, R.J. (2006) Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers, Ecology Letters, vol 9, pp 615-629. Slatkin, M. (1995) A measure of population subdivision based on microsatellite allele frequencies, Genetics Society of America, vol 139, pp 457-462. Smith, K. & Weldon, C. (2007) A conceptual framework for detecting oral Chytridiomycosis in tadpoles, Copeia, vol 2007, pp 1024-1028. Spear, S., Peterson, C., Matocq, M. & Storfer, A. (2005) Landscape genetics of the blotched tiger salamander (Ambystoma tigrinum melanostictum), Molecular Ecology, vol 14, pp 2553- 2564. Storfer, A. (2003). Amphibian declines: future directions, Diversity and Distributions, vol 9, pp 151-163. Stuart, L.C. (1948) The Amphibians and Reptiles of Alta Verapaz, Guatemala, Miscellaneous Publications, Museum of Zoology University of Michigan, vol 69, pp 1–109. Stuart, L.C. (1953) A description of a sub humid corridor across northern Central America, with comments on its herpetofaunal indicators, Michigan: Contributions from the Laboratory of Vertebrate Biology, University of Michigan, Ann Arbor. Stuart, L.C. (1963) A checklist of the herpetofauna of Guatemala, Miscellaneous Publications, Museum of Zoology University of Michigan, vol 122, pp 1–150. Stuart, S.N., Chanson, J.S., Cox, N.A., Young, B.E., Rodriguez, A.S., Fischman, D.L. & Waller, R.W. (2004) Status and trends of amphibian declines and extinctions world-wide, Science, vol 306, pp 1783-1786.
57 Toonen, R.J., & Hughes, S. (2001) Increased Throughput for Fragment Analysis on ABI Prism 377 Automated Sequencer Using a Membrane Comb and STRand Software, Biotechniques, vol 31, pp 1320-1324. Tyler, M.J. & Carter D. (1981) Oral birth of the young of the gastric brooding frog Rheobatrachus silus, Animal Behaviour, vol 29, pp 280-282. Tyler, M.J., Sheaman, D., Franco, R., O’Brien, P. & Seamark, R. (1983) Inhibition of gastric acid secretion in the gastric brooding frog, Rheobatrachus silus, Science, vol 222, pp 609- 610. Tyler, M.J., Wassersug, R. & Smith, B. (2007) How frogs and humans interact: influences beyond habitat destruction, epidemics and global warming, Applied Herpetology, vol 4, pp 1- 18. Untergrasser, A., Cutcutache, I., Koressaar, T., Ye, J., Faircloth, B.C., Remm, M., Rozen, R.G. (2012) Primer3-new capabilities and interfaces, Nucleic Acids Research , vol 40 Valencia-Aguilar, A., Cortés-Gómez, A.M. & Ruiz-Agudelo, C.A. (2013) Ecosystem services provided by amphibians and reptiles in Neotropical ecosystems, International Journal of Biodiversity Science,Ecosystem Services & Management, vol 9 pp 257-272. Van De Vliet, M.S., Diekmann, O.E., Serraão, E.A. & Beja, P. (2009), Isolation of highly polymorphic microsatellite loci for a species with a large genome size: sharp-ribbed salamander (Pleurodeles waltl), Molecular Ecology Resources, vol 9, pp 425-428. Van Den Bussche, R.A., Lack, J.B., Stanley Jr., C.E., Wilkinson, J.E., Truman, P.S., Smith, L.M. & McMurry, S.T. (2009) Development and characterization of 10 polymorphic tetranucleotide microsatellite markers for New Mexico Spadefoot toads (Spea multiplicata), Conservation Genetics Resources, vol 1, pp71-73. Vázquez, P.E., Méndez, R.M., Retana, O.G. & Naranjo, E.J. (2006) Uso medicinal de la fauna silvestre en los altos de Chiapas, México, Interciencia, vol 31, pp 491-499. Velo-Antón, G., Windfield, J.C., Zamudio, K. & Parra-Olea, G. (2009) Microsatelite markers for Pseudoeurycea leprosa, a plethodontid salamander endemic to the Transmexican Neovolcanic Belt, Conservation Genetics Resources, vol 1, pp 5-7. Voyles, J., Young, S., Berger, L., Campbell, C., Voyles, W., Dinudom, A., Cook, D., Webb, R., Alford, R., Skerratt, L. & Speare, R. (2009) Pathogenesis of Chytridiomycosis, a cause of catastrophic amphibian declines, Science, vol 326, pp 582-585. Wallace, K.J. (2007) Classification of ecosystem services: Problems and solutions, Biological Conservation, vol 139, pp 235-246. Whiles, M.R., Lips, K.R., Pringle, C.M., Kilham, S.S., Bixby, R.J., Brenes, R., Conelly, S., Colon-Gaud, J.C., Hunte-Brown, M., Huryn A.D., Huryn, A.D., Montgomery, C. & Peterson, S. (2006) The effects of amphibian population declines on the structure and function of Neotropical stream ecosystems, Frontiers in Ecology and the Environment, vol 4, pp 27-34. Whitfield, S.M., Bell, K.E., Philippi, T., Sasa, M., Bolaños, F., Chaves, G., Savage, J.M. & Donnelly M.A. (2007) Amphibian and reptile declines over 35 years at La Selva, Costa Rica, Proceedings of the National Academy of Science, vol 104, pp 8352–8356. Zamora, O.A. (2011) Evaluación de la presencia del hongo quitridio Batrachochytrium dendrobatidis en anfibios (Anura y Caudata) en siete departamentos y su distribución potencial en Guatemala, Thesis to obtain the Licentiate degree in Biology, Universidad del Valle de Guatemala Zane, L., Bargellini, L. & Patarnello, T. (2002) Strategies for microsatellite isolation: a review, Molecular Ecology, vol 11, pp 1-16
58 Zhang, W., Chen, J., Yang, Y., Tang, Y., Shang, J. and Shen B. (2011) A practical comparison of De Novo Genome Assembly Software Tools for Next-Generation Sequencing Technologies, PLoS ONE, vol 6, pp 1-12.
59
Appendices
Table 5. Complete set of primers tested for in this project
Primer name Forward sequence Reverse sequence TF04_D05 [6-FAM]GTTTGATTGGTCACCTTTCTCC TGGCAACATCAAATCCTATCAC TF04_C11 [HEX]CGTGGACCTTCTATGTTGCTG GGCATATGTGTACCCACTACCAG TF08_E11 [6-FAM]CAGGGACACAGGAAATGGTC TTCATGTGAAGCACCTGAGTG TF08_F10 [HEX]ATTTAGAATTGACCATAACAGAAAGG CTTCTGGCCACTGGTAGATAAG TF08_D02 [6-FAM]GATCCGTTCATGAGATGCAG ACTGTCACATGAGATTGCTTCTG TF08_C10 [HEX]CAGGTTCCAGCCACAAGAAG CACCACTCAGGCTGTGTAACC TF08_D11 [6-FAM]AGCTGCTTGTAAAGAGATAACTTGC GAGACCTGGGCATTATGGAG TF08_B07 [HEX]TGATGTCACCACCTCTGACC CACCTCATTCTTCCCTAAATGC TF08_D03 [6-FAM]TGCCGAGAACACTGAGTAATG AAACAATATGTTTTAATAGTTCGGACA TF08_B02 [HEX]ACAGACAGGCAGTTGGATTTC AAACTCTAGTTCAGCCAAACAGC TF08_E07 [6-FAM]CCAAATACCAGCTCTTCTAGACC GAAACTAAATTTAATGAGGAGGATACC TF08_C07 [HEX]GATCAATAAGGGTTCAAGTATGTGC CAGCCAGTACTAGATGACAGTTGG TF04_C09 [6-FAM]GACACTGAGCTTTCCTGGTGT TTTCTATGGAACAGGAAGTGTCAT TF08_D05 [HEX]AGGAATTAAAGGCCGTACCC AATAGTGGCCCGTGTTGG TF08_B04 [6-FAM]AACAGGTAAGGAATTTGTAAGACAGG AGCACTTCGAGAATGCCAAG TF08_B10 [HEX]GTCATGAAGAATGAAGGCTGTTAC GGTCAGAACATGAGCATTGTG 2TR I* GAGAGGTGACCAGAGAGAGCG TCTCTCTTCACACCCAGATTATCC 2TR II* ATGTGGCCTATGGCAAGAGC GAAGCCGAGAGACACACACC 2TR III* [6-FAM]CCACGGATCAAACACACCC CGGACGGTGTGAACTTAGCC 2TR IV* TCTCTCCGCCTGTCTAACCC AAACACAACAGAAGGGTGCG 2TR V* CTGTGAACAAGATAGGATTGCC TCTTGTTCCCTGTATAGGCCC 2TR VI* CACCTATGGTCCTTCTCTACACTGC TCTACCCACAGTGCACAGCC 2TR VII* [HEX]CACATGCACACAATGCCTCC GGGCTTTGGTGTGTGGGG
2TR VIII* CCTTTCTTGTTGGTGTGTCCG CACACATAATCCCCGGATGC 3TR I* CACAGCCACACATCTCCACC CGGCTGTGTTGGGGTAGG 3TR II* CCTGCTGTTCCCTGTGTATCC AAGGAGGAGGACAGAAGCCG 3TR III* [6-FAM]ATCTCAGTGGAGGCACACCC GGAAGAGCAGGAGCAAGACC 3TR IV* [6-FAM]CACATCACAGGACACGGACC AGGCGGATATACCTGTCCCG
3TR V* [HEX]TACGGAAAACTCTGTCCCCG CAGAGGTGTCCGGAGAAGG 3TR VI* [HEX]TGAGGGTAGGATGATGATGG CACAATGATGACCAAACACG 3TR VII* [HEX]GATGGATAAGAAGGAGGAGGG GCTATCAGGGAAATTCATGGC 4TR I* [6-FAM]TTCCTACTTGCACGACTGCC GCTCCAAGTTGTTGGCTTCC 4TR II* [6-FAM]CACCTATCTGGGAGTGCTGC CAGGTATCTCCGGCTCTTCC
4TR III* [6-FAM]AACACACACCTCCCTCCTCC TATGTATGCAGCAGAGCGGG 4TR IV* [HEX]ATACAGAGCTCCGAGACCACG CGTGATCCATCCCTCCCC 4TR V* CAGCTCGGGGACTTATAACC AGATAATAGACAGAACGCTACGG 4TR VI* [HEX]TCTATGTATCATCCATCCTGCC CAGACATACAGGTGTGTGATGG *Primers developed from NGS data
61
Table 6. STRand results for the nine loci Sam Populat TF04_D TF04_ TF08_ TF08_ TF08_ TF08_ TF08_ 2TR 2TR ple ion 05 C11 C10 D11 B07 B02 B04 III VII 117 Laj Chimel 254 258 254 258 0 0 206 214 98 106 120 122 92 174 150 150 148 150 118 Laj Chimel 254 258 286 290 0 0 206 206 104 104 118 138 92 174 150 150 148 150 119 Laj Chimel 260 264 264 288 168 172 214 216 98 106 136 144 174 174 150 150 150 150 120 Laj Chimel 256 264 0 0 170 170 0 0 98 106 142 146 92 174 150 150 0 0 121 Laj Chimel 258 258 258 258 0 0 0 0 98 104 0 0 174 174 150 150 0 0 122 Laj Chimel 254 258 258 258 0 0 206 216 0 0 120 122 92 174 150 150 0 0 123 Laj Chimel 256 258 258 302 198 198 204 214 98 106 120 122 174 174 150 150 0 0 124 Laj Chimel 258 258 0 0 0 0 0 0 98 104 0 0 92 174 150 150 0 0 125 Laj Chimel 258 278 258 278 0 0 204 206 104 104 120 122 92 174 150 150 0 0 126 Laj Chimel 0 0 0 0 0 0 0 0 98 104 120 120 92 174 148 150 148 150 127 Laj Chimel 254 258 258 294 196 204 204 214 0 0 120 120 174 174 150 150 150 150 128 Laj Chimel 258 262 282 294 212 214 204 206 98 104 120 122 92 174 130 140 150 150 129 Laj Chimel 258 258 258 302 198 210 208 214 104 104 120 122 92 174 150 150 148 150 130 Laj Chimel 252 258 294 294 198 212 206 214 104 104 120 120 92 174 148 150 148 150 131 Laj Chimel 0 0 0 0 212 222 204 206 104 104 120 122 92 178 150 150 148 150 132 Laj Chimel 258 258 258 258 198 200 204 206 98 104 120 122 92 174 150 150 150 150 133 Laj Chimel 254 262 294 302 210 212 206 214 104 104 120 122 92 174 150 150 0 0 134 Laj Chimel 252 258 0 0 0 0 204 212 104 104 0 0 92 174 150 150 148 150 135 Laj Chimel 0 0 0 0 0 0 204 206 104 104 120 120 174 174 150 150 148 150 136 Laj Chimel 258 262 0 0 198 210 204 206 98 104 0 0 92 174 150 150 148 150 137 Laj Chimel 258 258 0 0 0 0 204 206 98 104 120 122 174 174 148 148 0 0 138 Laj Chimel 0 0 258 258 0 0 0 0 0 0 0 0 92 174 150 150 150 150 1 Laj Chimel 268 290 270 284 226 232 198 200 98 106 0 0 92 174 126 144 126 144 2 Laj Chimel 268 290 270 284 226 232 198 200 98 106 0 0 92 174 126 144 126 144 92 Trifinio 244 244 256 256 160 160 188 188 98 106 0 0 92 174 126 126 126 126 93 Trifinio 272 282 256 258 160 162 0 0 0 0 0 0 92 174 126 126 124 126 139 Trifinio 286 298 258 258 158 160 192 194 0 0 128 132 92 174 126 126 126 126 140 Trifinio 256 264 0 0 160 160 196 198 98 106 0 0 92 174 126 126 0 0 141 Trifinio 270 274 0 0 160 160 196 196 98 98 128 128 92 174 126 126 126 126 142 Trifinio 260 286 256 258 158 160 0 0 0 0 128 128 92 174 126 126 126 126 143 Trifinio 282 290 258 258 158 160 196 198 0 0 112 120 92 174 124 124 124 124 144 Trifinio 270 282 256 258 160 162 0 0 98 106 118 120 92 174 126 126 124 126 145 Trifinio 260 260 0 0 160 160 194 196 0 0 0 0 92 174 126 126 0 0 146 Trifinio 260 260 254 256 154 154 0 0 0 0 128 128 92 92 126 126 126 126 147 Trifinio 266 270 266 270 198 198 196 196 0 0 0 0 92 174 126 156 126 138 148 Trifinio 260 270 256 258 154 160 196 198 98 106 128 128 92 174 126 126 126 126 149 Trifinio 268 274 254 256 154 160 208 210 98 98 0 0 92 174 126 126 124 126 150 Trifinio 274 298 254 254 160 160 210 212 0 0 0 0 92 174 126 126 126 126 151 Trifinio 262 266 254 256 160 160 194 196 0 0 112 112 92 92 126 126 124 126 152 Trifinio 262 278 0 0 154 160 194 196 0 0 128 128 174 174 126 126 124 126 153 Trifinio 264 274 254 254 154 160 192 194 98 106 118 122 92 174 126 126 124 126 154 Trifinio 256 274 256 256 160 160 0 0 0 0 102 102 174 174 126 126 0 0 155 Trifinio 260 274 254 260 160 220 0 0 0 0 128 128 92 174 126 126 124 126 156 Trifinio 274 290 254 274 160 160 194 196 98 106 112 128 92 174 126 126 124 126 157 Trifinio 244 266 254 256 154 160 0 0 0 0 128 128 174 174 126 126 0 0 158 Trifinio 290 294 254 256 160 162 0 0 0 0 128 128 174 174 126 126 124 126 159 Trifinio 274 274 254 274 160 160 194 196 0 0 0 0 174 174 126 126 126 126 162 Trifinio 260 278 254 256 160 160 194 196 98 106 112 136 92 174 126 126 124 126 163 Trifinio 248 268 256 268 154 160 194 196 98 106 128 128 92 174 126 126 124 126 164 Trifinio 264 268 256 264 160 160 0 0 0 0 0 0 92 174 126 126 0 0 165 Trifinio 260 274 254 260 160 160 0 0 98 106 0 0 92 92 126 126 126 126 167 Trifinio 268 270 258 270 160 198 0 0 98 98 0 0 174 174 126 126 124 126 63 168 Trifinio 262 270 254 254 160 160 188 190 98 106 0 0 92 92 126 126 126 126 169 Trifinio 260 274 254 256 154 160 188 190 98 106 0 0 92 92 0 0 0 0 170 Trifinio 266 294 254 266 160 160 192 196 0 0 0 0 92 174 126 126 126 126 171 Trifinio 266 270 0 0 198 198 0 0 0 0 0 0 174 174 126 126 126 136 172 Trifinio 270 274 256 256 154 160 194 196 98 106 128 128 92 174 126 126 126 126 175 Trifinio 244 274 254 256 160 160 0 0 0 0 0 0 92 174 126 126 126 126 176 Trifinio 274 282 0 0 160 160 0 0 98 98 0 0 92 174 126 126 126 126 178 Trifinio 270 274 258 258 160 160 0 0 0 0 0 0 92 174 126 126 126 126 179 Trifinio 268 298 258 266 160 160 194 194 0 0 128 132 92 174 126 126 126 126 180 Tatasiriré 0 0 0 0 158 162 0 0 98 102 0 0 174 174 142 154 114 142 181 Tatasiriré 286 290 258 258 158 158 202 208 0 0 128 130 174 174 0 0 114 152 182 Tatasiriré 282 286 252 258 158 158 208 208 98 102 126 134 174 174 0 0 114 114 183 Tatasiriré 294 310 252 258 140 160 202 202 0 0 0 0 92 174 0 0 114 114 184 Tatasiriré 290 294 258 262 158 162 208 208 0 0 128 128 92 174 150 152 114 152 185 Tatasiriré 260 278 258 258 0 0 0 0 98 102 124 126 0 0 142 144 114 142 186 Tatasiriré 282 290 256 258 158 158 202 210 98 106 0 0 92 174 150 152 114 150 187 Tatasiriré 290 294 258 258 158 158 0 0 0 0 0 0 92 174 138 140 114 138 188 Tatasiriré 278 286 252 258 158 158 0 0 0 0 0 0 92 174 142 148 148 154 189 Tatasiriré 282 294 252 258 158 158 200 202 0 0 124 126 92 174 152 156 114 154 190 Tatasiriré 270 282 252 270 158 162 0 0 98 106 0 0 174 174 148 150 148 150 191 Tatasiriré 0 0 0 0 158 162 0 0 100 102 120 120 0 0 148 150 114 148 192 Tatasiriré 0 0 0 0 0 0 192 194 98 102 0 0 0 0 0 0 0 0 193 Tatasiriré 260 286 258 258 158 162 200 202 98 102 0 0 174 174 134 136 114 136 194 Tatasiriré 278 286 252 258 158 158 200 202 0 0 0 0 174 174 0 0 114 114 195 Tatasiriré 290 294 258 258 158 162 200 202 0 0 128 128 92 174 142 144 114 144 196 Tatasiriré 0 0 258 258 0 0 0 0 92 100 0 0 0 0 0 0 114 154 197 Tatasiriré 260 294 252 258 158 158 200 202 98 104 124 126 92 174 148 150 114 150 198 Tatasiriré 270 288 258 258 158 158 0 0 98 104 0 0 92 92 0 0 0 0 64 199 Tatasiriré 0 0 258 258 158 158 208 208 0 0 0 0 92 174 142 154 0 0 200 Tatasiriré 260 300 252 258 158 158 0 0 0 0 120 128 92 174 150 156 114 150 201 Tatasiriré 260 260 258 258 158 162 0 0 0 0 120 128 0 0 148 150 114 148 202 Tatasiriré 294 300 252 258 158 158 200 202 98 106 128 128 174 174 144 146 114 144 203 Tatasiriré 278 302 250 258 158 158 0 0 0 0 124 126 92 174 150 156 114 150 204 Tatasiriré 0 0 258 258 0 0 0 0 100 102 0 0 0 0 152 156 114 156 205 Tatasiriré 290 310 258 258 158 162 202 214 0 0 0 0 92 174 0 0 114 114 206 Tatasiriré 0 0 0 0 158 158 0 0 98 106 126 128 92 174 142 144 114 144 207 Tatasiriré 278 282 258 258 158 158 0 0 0 0 0 0 0 0 0 0 0 0 208 Tatasiriré 290 302 252 258 158 162 200 202 0 0 0 0 92 174 140 150 114 140 209 Tatasiriré 292 302 252 258 0 0 200 202 98 102 0 0 0 0 142 144 114 144 210 Tatasiriré 0 0 256 256 158 158 206 208 98 106 120 122 174 174 148 150 114 148 211 Tatasiriré 0 0 258 258 158 158 0 0 98 98 0 0 92 174 0 0 0 0 212 Tatasiriré 290 294 252 258 0 0 0 0 0 0 0 0 0 0 0 0 0 0
65
Table 7. P-values for the Hardy-Weinberg Analysis
Locus Laj Chimel Trifinio Tatasiriré TF04_D05 0.15624 0.54317 0.53078 TF04_C11 0.02183 0.05816 0.09059 TF08_C10 0.1391 0.14727 0.02344
TF08_D11 0.0337 0.00181 0 TF08_B07 0.00194 0.03661 0.02816 TF08_B02 0.00092 0.00012 0.01503 TF08_B04 0.00645 0.05172 0.17483 2TR III 0 0.01498 0.00131
2TR VII 0.00156 1 0.99825
Table 8. AMOVA results per loci for the three populations
Locus Variation Variation within among pop population TF04_D05 11.57137 88.42863 TF04_C11 18.85939 81.14061 TF08_C10 46.32529 53.67471 TF08_D11 20.53585 79.46415
TF08_B07 19.00374 80.99626 TF08_B02 22.64184 77.35816 2TR III 57.71009 42.28991 42.89572 57.10428 2TR VII
Table 9. Allele range for each locus
Locus Allele range TF04_D05 244-310 TF04_C11 250-320 TF08_C10 140-232 TF08_D11 188-216 TF08_B07 92-106 TF08_B02 102-146 TF08_B04 92-176 2TR III 124-156 2TR VII 114-156
Table 10. Localities, coordinates and samples collected
Locality Municipality, Longitude Latitude Elevation Sample Species Researcher Collectors Type of sample department (m.a.s.l) No. No. Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 1 Plectrohyla matudai NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 2 Plectrohyla matudai OAZ 096 AZ, EL, MM, Liver tissue Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 3 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 4 Plectrohyla matudai NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 5 Plectrohyla sagorum OAZ 097 AZ, EL, MM, Liver tissue Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 6 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 7 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 8 Plectrohyla matudai NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 9 Plectrohyla sagorum OAZ 098 AZ, EL, MM, Liver tissue Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 10 Plectrohyla matudai NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 11 Plectrohyla matudai NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 12 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 13 Plectrohyla matudai NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 14 Plectrohyla sagorum OAZ 099 AZ, EL, MM, Liver tissue Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 15 Plectrohyla matudai NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 16 Plectrohyla matudai NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 17 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 18 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 19 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 20 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 21 Plectrohyla sagorum 0AZ 103 AZ, EL, MM, Liver tissue Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 22 Plectrohyla matudai NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 23 Plectrohyla matudai NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 24 Plectrohyla NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 25 Plectrohyla matudai OAZ 104 AZ, EL, MM, Liver tissue Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 26 Plectrohyla matudai OAZ 107 AZ, EL, MM, Liver tissue Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 27 Plectrohyla avia OAZ 100 AZ, EL, MM, Liver tissue Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 28 Plectrohyla sagorum OAZ 105 AZ, EL, MM, Liver tissue Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 29 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 30 Ptychohyla? OAZ 101 AZ, EL, MM, Liver tissue Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 31 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 32 Plectrohyla matudai NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 33 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM
68 Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 34 Plectrohyla OAZ 108 AZ, EL, MM, Liver tissue Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 35 Plectrohyla avia OAZ 102 AZ, EL, MM, Liver tissue Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 36 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 37 Plectrohyla OAZ 109 AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 38 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 39 Plectrohyla sagorum NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM Tonishaje river, Tajumulco, San -91.94748 15.04633 1779 ADN 40 Plectrohyla matudai NC AZ, EL, MM, Buccal swab Buenos Aires Village Marcos FM El Tocapote Forest Sibinal, San -92.0581 15.13798 2547 ADN 41 Plectrohyla ERL 01 AZ, EL Buccal swab Marcos glandulosa? El Tocapote Forest Sibinal, San -92.0581 15.13798 2547 ADN 42 Plectrohyla ERL 02 AZ, EL Buccal swab Marcos glandulosa? El Tocapote Forest Sibinal, San -92.0581 15.13798 2547 ADN 43 Plectrohyla ERL 03 AZ, EL Liver tissue Marcos glandulosa? El Tocapote Forest Sibinal, San -92.0581 15.13798 2547 ADN 44 Incillius OAZ 110 AZ, EL Liver tissue Marcos Canjulá Regional Sibinal, San -92.0548 15.13303 2685 ADN 45 Plectrohyla ERL 04 AZ, EL Liver tissue Forest Marcos glandulosa? Canjulá Regional Sibinal, San -92.0548 15.13303 2685 ADN 46 Plectrohyla ERL 05 AZ, EL Liver tissue forest Marcos glandulosa? Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 47 Ptychohyla OAZ 119 AZ, EL, MO, Liver tissue Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 48 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 49 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 50 Ptychohyla OAZ 118 AZ, EL, MO, Liver tissue Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 51 Lithobates OAZ 117 AZ, EL, MO, Liver tissue Huehuetenango GP
69 Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 52 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 53 Lithobates OAZ 111 AZ, EL, MO, Liver tissue Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 54 Lithobates ERL O6 AZ, EL, MO, Liver tissue Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 55 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 56 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 57 Lithobates OAZ 112 AZ, EL, MO, Liver tissue Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 58 Lithobates OAZ 113 AZ, EL, MO, Liver tissue Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 59 Lithobates OAZ 114 AZ, EL, MO, Liver tissue Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 60 Ptychohyla OAZ 115 AZ, EL, MO, Liver tissue Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 61 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 62 Lithobates OAZ 116 AZ, EL, MO, Liver tissue Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 63 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 64 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 65 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 66 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 67 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 68 Ptychohyla OAZ 120 AZ, EL, MO, Liver tissue Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 69 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP
70 Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 70 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 71 Craugator ERL 07 AZ, EL, MO, Liver tissue Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 72 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 73 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 74 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 75 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 76 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 77 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 78 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 79 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 80 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 81 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca La Bolsa La Libertad, -91.94255 15.58241 1270 ADN 82 Lithobates NC AZ, EL, MO, Buccal swab Huehuetenango GP Finca El Injerto La Libertad, -91.943861 15.55688 1510 ADN 83 Lithobates NC AZ, EL, MO Buccal swab Huehuetenango Finca El Injerto La Libertad, -91.943861 15.55688 1510 ADN 84 Lithobates NC AZ, EL, MO Buccal swab Huehuetenango La Cumbre, Finca El La Libertad, -91.943861 15.55688 2198 ADN 85 Plectrohyla OAZ 121 AZ, EL, MO Liver tissue Injerto Huehuetenango La Cumbre, Finca El La Libertad, -91.943861 15.55688 2198 ADN 86 Plectrohyla ERL 08 AZ, EL, MO Liver tissue Injerto Huehuetenango La Cumbre, Finca El La Libertad, -91.943861 15.55688 2198 ADN 87 Ptychohyla OAZ 122 AZ, EL, MO Liver tissue Injerto Huehuetenango
71 La Cumbre, Finca El La Libertad, -91.943861 15.55688 2198 ADN 88 Plectrohyla OAZ 123 AZ, EL, MO Liver tissue Injerto Huehuetenango Los Tarrales Natural Patulul, -91.13333 14.51667 1500 ADN 91 Ptychohyla NC AZ, MO Buccal swab Reserve Suchitepéquez Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 92 Plectrohyla OAZ 126 AZ, MO Liver tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -90.79208 15.5122 1700 ADN 93 Plectrohyla OAZ 127 AZ, MO Liver tissue Trifinio, Chiquimula guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 94 Plectrohyla hartwegii OAZ 128 AZ, MO Liver tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 95 Plectrohyla hartwegii OAZ 129 AZ, MO Liver tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 96 Plectrohyla hartwegii OAZ 130 AZ, MO Liver tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 97 Plectrohyla quecchi OAZ 131 AZ, MO Liver tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 98 Plectrohyla quecchi OAZ 132 AZ, MO Liver tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 99 Plectrohyla OAZ 133 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 100 Plectrohyla OAZ 133 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 101 Plectrohyla OAZ 134 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 102 Plectrohyla OAZ 135 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 103 Plectrohyla OAZ 136 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 104 Plectrohyla OAZ 137 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 105 Plectrohyla OAZ 138 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 106 Plectrohyla OAZ 139 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 107 Plectrohyla OAZ 140 AZ, MO, PLU Tail tissue Laj Chimel
72 Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 108 Plectrohyla OAZ 141 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 109 Plectrohyla OAZ 142 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 110 Plectrohyla OAZ 143 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 111 Plectrohyla OAZ 144 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 112 Plectrohyla OAZ 145 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 113 Plectrohyla OAZ 146 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 114 Plectrohyla OAZ 147 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 115 Plectrohyla OAZ 148 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 116 Plectrohyla OAZ 149 AZ, MO, PLU Tail tissue Laj Chimel Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 117 Plectrohyla OAZ 150 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 118 Plectrohyla OAZ 151 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 119 Plectrohyla OAZ 152 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 120 Plectrohyla OAZ 153 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 121 Plectrohyla OAZ 154 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 122 Plectrohyla OAZ 155 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 123 Plectrohyla OAZ 156 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 124 Plectrohyla OAZ 157 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 125 Plectrohyla OAZ 158 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis
73 Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 126 Plectrohyla OAZ 159 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 127 Plectrohyla OAZ 160 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 128 Plectrohyla OAZ 161 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 129 Plectrohyla OAZ 162 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 130 Plectrohyla OAZ 163 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 131 Plectrohyla OAZ 164 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 132 Plectrohyla OAZ 165 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 133 Plectrohyla OAZ 166 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 134 Plectrohyla OAZ 167 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 135 Plectrohyla OAZ 168 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 136 Plectrohyla OAZ 169 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 137 Plectrohyla OAZ 170 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 138 Plectrohyla OAZ 171 AZ, MO, PLU Tail tissue Laj Chimel guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 139 Plectrohyla OAZ 172 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 140 Plectrohyla OAZ 173 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 141 Plectrohyla OAZ 174 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 142 Plectrohyla OAZ 175 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 143 Plectrohyla OAZ 176 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis
74 Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 144 Plectrohyla OAZ 177 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 145 Plectrohyla OAZ 178 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 146 Plectrohyla OAZ 179 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 147 Plectrohyla OAZ 180 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 148 Plectrohyla OAZ 181 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 149 Plectrohyla OAZ 182 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 150 Plectrohyla OAZ 183 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 151 Plectrohyla OAZ 184 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 152 Plectrohyla OAZ 185 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 153 Plectrohyla OAZ 186 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 154 Plectrohyla OAZ 187 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 155 Plectrohyla OAZ 188 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 156 Plectrohyla OAZ 189 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 157 Plectrohyla OAZ 190 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 158 Plectrohyla OAZ 191 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 159 Plectrohyla OAZ 192 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 160 Plectrohyla OAZ 193 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 161 Plectrohyla OAZ 194 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis
75 Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 162 Plectrohyla OAZ 195 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 163 Plectrohyla OAZ 196 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 164 Plectrohyla OAZ 197 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 165 Plectrohyla OAZ 198 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 166 Plectrohyla OAZ 199 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 167 Plectrohyla OAZ 200 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 168 Plectrohyla OAZ 201 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 169 Plectrohyla OAZ 202 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 170 Plectrohyla OAZ 203 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 171 Plectrohyla OAZ 204 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 172 Plectrohyla OAZ 205 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 173 Plectrohyla OAZ 206 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 174 Plectrohyla OAZ 207 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 175 Plectrohyla OAZ 208 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 176 Plectrohyla OAZ 209 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 177 Plectrohyla OAZ 210 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 178 Plectrohyla OAZ 211 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 179 Plectrohyla OAZ 212 AZ, MO, CS Tail tissue Trifinio, Chiquimula guatemalensis
76 Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 180 Plectrohyla OAZ 213 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 181 Plectrohyla OAZ 214 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 182 Plectrohyla OAZ 215 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 183 Plectrohyla OAZ 215 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 184 Plectrohyla OAZ 216 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 185 Plectrohyla OAZ 217 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 186 Plectrohyla OAZ 218 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 187 Plectrohyla OAZ 219 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 188 Plectrohyla OAZ 220 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 189 Plectrohyla OAZ 221 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 190 Plectrohyla OAZ 222 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 191 Plectrohyla OAZ 223 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 192 Plectrohyla OAZ 224 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 193 Plectrohyla OAZ 225 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 194 Plectrohyla OAZ 226 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 195 Plectrohyla OAZ 227 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 196 Plectrohyla OAZ 228 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 197 Plectrohyla OAZ 229 AZ Tail tissue Eco-touristic Park guatemalensis
77 Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 198 Plectrohyla OAZ 230 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 199 Plectrohyla OAZ 231 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 200 Plectrohyla OAZ 232 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 201 Plectrohyla OAZ 233 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 202 Plectrohyla OAZ 234 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 203 Plectrohyla OAZ 235 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 204 Plectrohyla OAZ 236 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 205 Plectrohyla OAZ 237 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 206 Plectrohyla OAZ 238 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 207 Plectrohyla OAZ 239 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 208 Plectrohyla OAZ 240 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 209 Plectrohyla OAZ 215 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 210 Plectrohyla OAZ 216 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 211 Plectrohyla OAZ 217 AZ Tail tissue Eco-touristic Park guatemalensis Tatasirire Waterfalls Jalapa, Jalapa -90.09546 14.56607 2100 ADN 212 Plectrohyla OAZ 218 AZ Tail tissue Eco-touristic Park guatemalensis Biosphere Reserve El Esquipulas, -89.3848 14.516 1700 ADN 213 Plectrohyla OAZ 219 AZ, MO, CS Tail tissue Trifinio Chiquimula guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 214 Plectrohyla OAZ 089 AZ Liver tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 215 Plectrohyla OAZ 090 AZ Liver tissue Laj Chimel guatemalensis
78 Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 216 Plectrohyla OAZ 091 AZ Liver tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 217 Plectrohyla OAZ 092 AZ Liver tissue Laj Chimel guatemalensis Cuatro Chorros river, Uspantán, Quiché -90.79208 15.5122 1700 ADN 218 Plectrohyla OAZ 093 AZ Liver tissue Laj Chimel guatemalensis *AZ: Alejandra Zamora;CS: Cirilo Súchite; EL: Erick López; FM: Fransisco Mazariegos; GP: Gabriel Pérez; MM: Maximino Mazariegos; MO: Milena Oliva; PLU: Pedro Ló *NC: Not collected
79
Figure 8. Investigation license extended by CONAP
80
Figure 9. Collection license extended by CONAP
81
Figure 10. Exportation license extended by CONAP
82