Survival, Movement and Growth of Juvenile Chinook ( Oncorhynchus tshawytscha) Salmon Over-wintering in Twitter Creek, South-central, Alaska

I investigated aspects of winter ecology of juvenile stream-type Chinook Salmon (Oncorhynchus tshawytscha) in Twitter Creek, a small tributary of the Anchor River, South-Central Alaska. A multistate modeling approach utilizing passive integrated transponder (PIT) technology to consider both live recaptures of individuals during discrete sampling occasions, and continuous data collected by monitoring movements of PIT-tagged fish was used to characterize both movement and survival. Juveniles emigrated from the stream throughout the winter peaking in the late fall and spring. Survival rates were eight times higher for juvenile Chinook that maintained stream fidelity during the entire winter period. The probability of emigration and survival were strongly size-dependent, where larger fish tended to remain resident and survive better than smaller fish which tended to migrate and experience higher mortality. The deceleration of growth of Pre-smolt (age 0+) Chinook Salmon during winter was accurately characterized by an asymtotic relationship between fish size and time-at-large during periods of low water temperatures. These results suggest that the increased growth and lower survival and tributary fidelity for a smaller juvenile Chinook could be a result of size-mediated, metaboloic rates and energy stores. The conflicts between maintaining energy stores and foraging require smaller individual to engage in risky energetically favorable behavior. These differences in physiological scaling result in size-dependent responses to winter induced food and space limitations. I suggest that tributary streams provide important winter habitats for dominate juvenile stream-type Chinook Salmon in the Anchor River drainage

New Methods to Estimate Abundance from Unmarked Populations Using Remote Camera Trap Data

Abundance estimates are central to the field of ecology and are an important tool for wildlife managers. While many tools are available for estimating abundance from individually identifiable animals, it is much more difficult to estimate abundance of unmarked animals. Most species have no natural markings and capturing them to apply artificial marks is invasive. One step toward noninvasive abundance estimation is the use of passive “traps” such as remote cameras or acoustic recording devices. The continuous-time data from these traps can be used to estimate abundance, although most available methods still require individually identifiable animals. There is a great need for methods to estimate abundance from unmarked populations using these trap data. We developed three methods for estimating abundance of unmarked animals from remote camera trap data. We worked outside the conventional capture-recapture framework to rethink how continuous remote data are handled. In Chapter 1, we developed an Instantaneous Sampling (IS) estimator based in sampling theory that treats remote camera data like point counts. In Chapter 2, we applied a time-to-event framework to develop a Space-to-Event (STE) and Time-to-Event (TTE) model to estimate abundance from trapping rate. We validated these methods on simulated populations with known abundance. All three methods produced unbiased estimates of abundance, regardless of animal movement rate. We performed a case study in which we estimated elk abundance from remote camera trap data in two study areas in Idaho. Estimates in one study area were comparable to an independent estimate of abundance from aerial surveys. In the other study area, other abundance methods are hard to implement, so our three models produced the first elk abundance estimates. The three methods developed here represent new ways of thinking about continuous-time remote camera data. These new methods allow biologists to estimate abundance from unmarked populations without tracking individuals over time. They have wide applications across species; biologists can select the method that best meets their specific circumstances. All three methods greatly reduce the amount of data required for analysis, which makes them practical management tools

Drought Influences Annual Survival of Painted Turtles in Western Nebraska

Aquatic habitats in the Great Plains of North America have increased risk of droughts under climate forecasts. Droughts have the potential to influence the population dynamics of pond turtles, and long-term studies are useful to assess the impact of climatic variation on turtles. We compiled twelve years of mark-recapture data for painted turtles (Chrysemys picta) captured in a pond in Keith County, Nebraska during 2005–2016 that included two periods of drought. We used a robust design analysis to investigate influences on population size, annual survival, temporary immigration, and capture probability. Estimates of the annual population size ranged from 92 (CI: 90–94) to 180 (CI: 175–186) but did not vary with drought conditions. Despite a relatively stable depth of water in our study pond, the probability of annual survival was reduced by 0.07 in females and 0.10 in males during drought years. Approximately one-fifth (temporary emigration probability: 0.19, CI = 0.16–0.23) of the population was outside the study pond at any given time. Our long-term research provides insights into the potential challenges to turtles in aquatic habitats undergoing prolonged changes in long-term climate conditions

Generalized estimators of avian abundance from count survey data

I consider modeling avian abundance from spatially referenced bird count data collected according to common protocols such as capture-recapture, multiple observer, removal sampling and simple point counts. Small sample sizes and large numbers of parameters have motivated many analyses that disregard the spatial indexing of the data, and thus do not provide an adequate treatment of spatial structure. I describe a general framework for modeling spatially replicated data that regards local abundance as a random process, motivated by the view that the set of spatially referenced local populations (at the sample locations) constitute a metapopulation. Under this view, attention can be focused on developing a model for the variation in local abundance independent of the sampling protocol being considered. The metapopulation model structure, when combined with the data generating model, define a simple hierarchical model that can be analyzed using conventional methods. The proposed modeling framework is completely general in the sense that broad classes of metapopulation models may be considered, site level covariates on detection and abundance may be considered, and estimates of abundance and related quantities may be obtained for sample locations, groups of locations, unsampled locations. Two brief examples are given, the first involving simple point counts, and the second based on temporary removal counts. Extension of these models to open systems is briefly discussed

A comparative study of enumeration techniques for free-roaming dogs in rural Baramati, District Pune, India

The presence of unvaccinated free-roaming dogs (FRD) amidst human settlements is a major contributor to the high incidence of rabies in countries such as India, where the disease is endemic. Estimating FRD population size is crucial to the planning and evaluation of interventions, such as mass immunisation against rabies. Enumeration techniques for FRD are resource intensive and can vary from simple direct counts to statistically complex capture-recapture techniques primarily developed for ecological studies. In this study we compared eight capture-recapture enumeration methods (Lincoln–Petersen’s index, Chapman’s correction estimate, Beck’s method, Schumacher-Eschmeyer method, Regression method, Mark-resight logit normal method, Huggin’s closed capture models and Application SuperDuplicates on-line tool) using direct count data collected from Shirsuphal village of Baramati town in Western India, to recommend a method which yields a reasonably accurate count to use for effective vaccination coverage against rabies with minimal resource inputs. A total of 263 unique dogs were sighted at least once over 6 observation occasions with no new dogs sighted on the 7th occasion. Besides this direct count, the methods that do not account for individual heterogeneity yielded population estimates in the range of 248–270, which likely underestimate the real FRD population size. Higher estimates were obtained using the Huggin’s Mh-Jackknife (437 ± 33), Huggin’s Mth-Chao (391 ± 26), Huggin’s Mh-Chao (385 ± 30), models and Application “SuperDuplicates” tool (392 ± 20) and were considered more robust. When the sampling effort was reduced to only two surveys, the Application SuperDuplicates online tool gave the closest estimate of 349 ± 36, which is 74% of the estimated highest population of free-roaming dogs in Shirsuphal village. This method may thus be considered the most reliable method for estimating the FRD population with minimal inputs (two surveys conducted on consecutive days)

Improving the Use of Migration Counts for Wildlife Population Monitoring

Counts of migrating animals are used to monitor populations, particularly for species that are not well sampled by breeding and wintering surveys. The use of migration counts for population monitoring relies on the assumptions that new individuals are detected each day, and that probability of detecting those individuals remains constant over time. The impact of violating these assumptions on our ability to estimate reliable population trends is not well understood. Further, on a broad spatial scale, our ability to combine data across sites to estimate regional or national trends has been limited by the possibility that trends vary regionally in an unknown way. Using simulated migration count data with known trend, I tested whether sampling effort (daily vs. non-daily sampling) and a temporal change in stopover duration (and thus detection probability) influenced our ability to estimate the known trend. I also tested whether analyzing data as hourly, daily or annual counts, or accounting for random error using analytical techniques, could improve accuracy and precision of estimated trends by reducing or better modeling variation in counts, respectively. Further, using model selection analytical techniques, I tested whether we could detect when trends vary regionally using current or increased number of sampling sites in a region. My findings show that trends can be improved for species with highly variable daily counts by sampling less frequently than daily or by aggregating hourly counts to annual totals. Commonly and rarely detected species were better analyzed as daily counts, collected daily throughout the migration. A linear increase in stopover duration over time biased trends and lead to a high probability of detecting an incorrect trend, which is only improved by both reducing sampling effort and including a covariate for stopover duration in regression analyses. Regional variation in trends can be detected, and increasing the length of the time series was more efficient for improving accuracy and precision of regional trends than increasing the number of sites sampled. Continued advancement of our knowledge of breeding origins and stopover duration of migrants are priorities for the further refinement of trends estimated using migration counts