Rapid technological advancements in digital cameras and widespread public access to the internet have inspired many researchers to consider alternative methods for collecting, analyzing, and distributing scientific data. Two emerging fields of study that have capitalized on these developments are “citizen science” and photo-id in wildlife capture-mark-recapture (CMR) studies. Both approaches offer unprecedented flexibility and potential for acquiring previously inconceivable datasets, yet both remain dependent on data collection by human observers. The absence of rigorous assessment of observer error rates causes many scientists to resist citizen science altogether or to fail to incorporate citizen-collected data into ecological analyses. This same need for consistent measurement and documentation of the type and frequency of errors resulting from different observers is mirrored in numerous ecological studies employing photographic identification. The driving question of interest behind this thesis rests at the intersection of these two fields: can citizen scientists provide an effective alternative to commonly utilized computer-assisted programs used with large photo-id databases from wildlife studies? To address this question we reviewed the history of wildlife photo-id in order to gain a better understanding of knowledge gaps caused by a failure to consistently report human error rates (Chapter 1). We then piloted a crowdsourcing approach to distributed photographic analysis by soliciting responses to image comparisons from a large number of untrained observers (Chapter 2). We found that observers correctly assessed 99.6% of all comparisons, but that the predictor variables for the two types of error (false positive and false negative) differed. Building upon a deeper understanding of the history, limitations, key issues, and recommendations for researchers considering using photo-id, we recommend the expanded use of citizen science methods as an effective alternative to computer-assisted approaches with large image libraries. Error rate improvements should allow scientists to more readily accept data collected by untrained observers as valid, and will also contribute to improved accuracy of ecological estimates of population size, vital rates, and overall conservation management of threatened or endangered species. Additionally, the general public will benefit from expanded opportunities to engage with and learn about the scientific process.

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For this study, the abundance of the salamander fauna in two streams was compared to determine the impact of logging activities. After five months of study, the area around one stream was logged by the National Forest Service. Following logging, the abundance of salamanders in the stream was greatly reduced, while the population in the undisturbed stream remained relatively unchanged. Six abundant species were used as indicators; two of these species (Desmognathus quadramaculatus and Desmognathus ochrophaeus) were negatively affected by the logging, while four species (Demognathus fuscus, Desmognathus monticola, Eurycea wilderae, and Gyrinophilus porphyriticus) were not affected. Body sizes of salamanders, used to represent age class, were analyzed to determine if the age/size structures of the populations were altered. This parameter was not impacted significantly by logging; all age/size classes of the populations were equally affected.

Cryptobranchus guildayi is described as an extinct species of large salamander that is closely related to the hellbender, Cryptobranchus alleganiensis. The validity of this extinct taxon has been questioned, so an expanded osteological sample of modern hellbenders was used for comparative purposes with the C. guildayi fossil material. Based on this analysis, all supposed distinguishing morphological characteristics used to define C. guildayi can be observed in specimens of C. alleganiensis, or are based on misidentifications. Therefore, Cryptobranchus guildayi is considered to be conspecific with C. alleganiensis and taxonomically should be considered a junior synonym of the latter. The reassignment of the C. guildayi specimens to C. alleganiensis and examination of undescribed fossil specimens from the same region expands the prehistoric range of the species to the Potomac River and its tributaries and also extends the age of the species to the Irvingtonian North American land mammal age.

Screening efforts at the Gray Fossil Site, Washington County, Tennessee, have yielded a unique and diverse salamander fauna for the southern Appalachian Mio-Pliocene; including at least five taxa from three modern families (Ambystomatidae, Plethodontidae, and Salamandridae) supporting the woodland-pond interpretation of the site. All specimens represent the earliest record of their respective families in the Appalachian Mountains; with the Notophthalmus sp. vertebrae being the only Mio-Pliocene skeletal fossil known for the family Salamandridae in North America. Three types of plethodontid salamander are present, with one type representing the earliest known desmognathine. The desmognathine fossils lend credence to the 'Appalachian' origin of the clade in the Mio-Pliocene. The GFS salamander fauna is predominated by plethodontids; competition is inferred by the presence of several similarly large-sized taxa and is invoked to explain the presence of neotenic individuals in an otherwise amicable terrestrial environment.

The salamander diversity in the southern Appalachian Mountains is now being threatened, with lungless salamanders (family Plethodontidae) likely the most vulnerable. Multiple environmental factors threaten Plethodontids, and environmental changes uniquely affect each species within this family. Unfortunately, there is a lack of understanding about the current status of salamanders in Greene County, TN that severely limits conservation efforts for lungless salamanders. This study examined local salamander populations’ current distributions and connected salamander abundance and species richness with a subset of environmental factors to determine what areas represent suitable habitat for different Plethodontids. This information was used to develop species distribution models, which were used to identify potential areas of salamander habitat to focus management and conservation efforts. Area constrained surveys were completed at 42 transects within four main areas using a robust, random sampling design. Abiotic and biotic data, along with salamander identification, were documented at each site. Four additional variables were acquired by geographic information system (GIS 10.6). Salamanders were present at 69% of the transects. Our results indicate that salamanders tend to be located at higher elevations, in ravines, and in thicker canopy cover areas. This work will be useful in long-term monitoring and future research as salamander population, habitat, and environmental changes continue to be monitored.

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I compared the performance of habitat models created from data of differing reliability. Because the reliability is dependent on the probability of detecting the species, I experimented to estimate detectability for a salamander species. Based on these estimates, I investigated the sensitivity of habitat models to varying detectability. Models were created using a database of amphibian and reptile observations at Fort A.P. Hill, Virginia, USA. Performance was compared among modeling methods, taxa, life histories, and sample sizes. Model performance was poor for all methods and species, except for the carpenter frog (Rana virgatipes). Discriminant function analysis and ecological niche factor analysis (ENFA) predicted presence better than logistic regression and Bayesian logistic regression models. Database collections of observations have limited value as input for modeling because of the lack of absence data. Without knowledge of detectability, it is unknown whether non-detection represents absence. To estimate detectability, I experimented with red-backed salamanders (Plethodon cinereus) using daytime, cover-object searches and nighttime, visual surveys. Salamanders were maintained in enclosures (n = 124) assigned to four treatments, daytime__low density, daytime__high density, nighttime__low density, and nighttime__high density. Multiple observations of each enclosure were made. Detectability was higher using daytime, cover-object searches (64%) than nighttime, visual surveys (20%). Detection was also higher in high-density (49%) versus low-density enclosures (35%). Because of variation in detectability, I tested model sensitivity to the probability of detection. A simulated distribution was created using functions relating habitat suitability to environmental variables from a landscape. Surveys were replicated by randomly selecting locations (n = 50, 100, 200, or 500) and determining whether the species was observed, based on the probability of detection (p = 40%, 60%, 80%, or 100%). Bayesian logistic regression and ENFA models were created for each sample. When detection was 80 __ 100%, Bayesian predictions were more correlated with the known suitability and identified presence more accurately than ENFA. Probability of detection was variable among sampling methods and effort. Models created from presence/absence data were sensitive to the probability of detection in the input data. This stresses the importance of quantifying detectability and using presence-only modeling methods when detectability is low. If planning for sampling as an input for suitability modeling, it is important to choose sampling methods to ensure that detection is 80% or higher. / Ph. D.