This study examines opportunities for renewable resource management when population data are collected by spatial subdivisions. In particular I look at potential applications for the design of management experiments, the distribution of monitoring resources, and the improvement of parameter estimation. Methods are developed to rank possible groupings of subdivisions for use as experimental units. Factors external to the experiment can cause differences between experimental units. Selecting subdivisions that have reacted similarly in the past to external factors could minimize the risk of external factors creating differences in experimental units. Methods are developed to identify subdivisions that could provide information about similar subdivisions when monitoring resources are low or when stratified sampling is being used. The use of these subdivisions as "index units" could notify managers of extremely good or bad years in a large number of subdivisions. Two methods developed by Walters (1986) provide innovative estimation techniques that can be used with subdivided populations. A Bayesian approach allows parameter estimates to be adjusted using a known distribution. Another approach allows similar subdivisions to be estimated jointly more accurately than would be possible individually. Not all renewable resource data sets provide reliable information for use with these applications. Data sets where there is little common variation, high levels of autocorrelation in the noise, or even modest amounts of measurement error are inappropriate for most methods. A series of steps is introduced for managers to test the reliability of the methods on their particular data sets. Data on Finnish reindeer (Rangifer tarandus tarandus) are used throughout the thesis to illustrate the methods. The reindeer data appear to be appropriate for these methods when tested using the steps developed. Possible experimental units and index units for monitoring are identified. Walters' (1986) methods of parameter estimation are used on the data set as well. The reindeer data show that subdivisions with similar external effects were located close to one another. This pattern was at least partially caused by the existence of extremely bad years occurring within geographic regions. The reindeer subdivisions are very highly managed and provide little evidence of any kind of density dependence. Managers could potentially benefit by conducting experiments to test the biological limits of the population growth rates and carrying capacities within subdivisions. / Science, Faculty of / Zoology, Department of / Graduate

A number of field studies suggest that some vertebrate populations are limited by spacing behaviour, , Small mammals of the genus Peromyscus and Microtus exhibit spacing behaviour by possessing home ranges, but they have contrasting patterns of population fluctuation, Deermice (Peromyscus sp.) fluctuate annually but maintain fairly constant numbers from year to year, whereas voles (Microtus sp.) 'cycle', reaching peak densities every 2 to 5 years. One use of the home range is for food-gathering, Therefore, these experiments were designed to investigate the influence of food availability on the home range and population dynamics of local deermice and voles (P. maniculatus austerus and M. townsendii). The addition of food in late winter resulted in a doubling of the number of deermice. Immigration was 2.5 times that of an unfed control. This could be explained since resident deermice reduced the size of their home ranges. Deermice populations with extra food increased their reproductive output compared with controls: larger numbers of mice bred, and for longer periods, more young were recruited, they grew faster and reached sexual maturity earlier. It is suggested that the onset and cessation of breeding in deermice are proximate responses to food availability, Deermouse dynamics may be closely tied to the temporal and spatial availability of food through the spatial organisation of individuals. It is also suggested that females, because of their energy demands for lactation, and their influence on the survival of young, may be more sensitive to these food conditions and hence exert a strong influence on deermouse population dynamics. Vole populations with, low- and intermediate-levels of food peaked at twice the control density, and a population with a high-level of food reached seven times control density. Voles immigrated to established populations, and colonized vacant areas in proportion to the food available. Like deermice, residents reduced the size of their ranges. Breeding was enhanced in all fed vole populations. Omnivorous deermice had larger ranges than did the herbivorous voles, but both species responded to extra food by reducing their range size, so the smallest deermouse ranges were the size of large vole ranges. The results indicate that home range size in both species is responsive to food availability, and that the concentration of food in the 'typical' habitats of these small mammals is different. If, as suggested, the heterogeneity of deermouse-food in the forest results in an annual cycle in numbers, then the reduced heterogeneity of vole-food in grasslands may influence vole dynamics. / Science, Faculty of / Zoology, Department of / Graduate

No description available.

No description available.

Understanding the role that extrinsic processes play in shaping animal population dynamics and demography is a central tenet of population ecology and an issue of vital importance for conservation and wildlife management. The top-down impact of predation and bottom-up influence of food availability are thought to be two of the most important extrinsic processes affecting population dynamics and demography of species occupying middle trophic levels. However, many studies only focus on quantifying the impact of one of these processes in isolation and it is not clear whether the impact of one extrinsic factor on population dynamics and demographic rates is augmented or lessened by changes in other extrinsic factors. In this thesis I examine the extent to which both top-down and bottom-up processes shape population dynamics (population size, recruitment and immigration) and demography (survival, reproduction, life-history trade-offs and reproductive strategies) in a long-lived species, the tawny owl, by taking advantage of a natural increase in predation risk (goshawk abundance) and a decline in food availability (field vole densities). Despite the increase in predation and the decline in food availability, the owl population remained stable, which we posit is due to goshawk predation being selective on individuals with a low reproductive value (juveniles and old individuals) and an increasing number of immigrants entering the population. Selective predation on older owls had a negative impact on the survival of this age class and appeared to be shaping the pattern of actuarial senescence and influencing the strength of the intrinsic trade-off between survival and reproduction. As food availability declined and predation risk increased owls appeared to be switching from an 'eggs in one basket strategy' of saving resources to invest more in fewer breeding attempts in the future, to a 'bet-hedging' strategy of reproducing more often, but investing less per breeding attempt.

A defining feature of capture-recapture is missing data due to imperfect detection of individuals. The standard approach used to deal with the missing data is to integrate (or sum) over all the possible unknown values. The missing data is completely removed and the resulting likelihood is in terms of the observed data. The problem with this approach is that often biologically unnatural parameters are chosen to make the integration (summation) tractable. A related consequence is that latent variables of interest, such as the population size and the number of births are only available as derived quantities. As they are not explicitly in the model they are not available to be used in the model as covariates to describe population dynamics. Therefore, models including density dependence are unable to be examined using standard methods. Instead of explicitly integrating out missing data, we choose to include it using data augmentation. Instead of being removed, the missing data is now present in the likelihood as if it were actually observed. This means that we are able to specify models in terms of the data we would like to have observed, instead of the data we actually did observe. Having the complete data allows us to separate the processes of demographic interest from the sampling process. The separation means that we can focus on specifying the model for the demographic processes without worrying about the sampling model. Therefore, we no longer need to choose parameters in order to simplify the removal of missing data, but we are free to naturally write the model in terms of parameters that are of demographic interest. A consequence of this is that we are able write complex models in terms of a series of simpler conditional likelihood components. We show an example of this where we fit a CJS model that has an individual-specific time-varying covariate as well as live re-sightings and dead recoveries. Data augmentation is naturally hierarchical, with parameters that are specified as random effects treated as any other latent variable and included into the likelihood. These hierarchical random effects models make it possible to explore stochastic relationships both (i) between parameters in the model, and (ii) between parameters and any covariates that are available. Including all of the missing data means that latent variables of interest, including the population size and the number of births, can now be included and used in the model. We present an example where we use the population size (i) to allow us to parameterize birth in terms of the per-capita birth rates, and (ii) as a covariate for both the per-capita birth rate and the survival probabilities in a density dependent relationship.

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No description available.

Access to abstract restricted until December 2014. / Acess to thesis restricted until December 2014. / Department of Biology

Conservation biology is primarily concerned with the amelioration of species decline. The Yellow-footed Rock-wallaby (Petrogale xanthopus xanthopus) is a medium sized Macropod that inhabits the semiarid rangelands of South Australia and New South Wales. Its conservation status is Vulnerable C2a(i). In this study, population modelling, spatially explicit habitat modelling and Population Viability Analysis (PVA) have been used to better understand the factors that affect the abundance and distribution of the P. x. xanthopus in South Australia. The processes that drive the population dynamics of a species operate at different scales. As such this research involves a collection of several inter-related and scale-specific empirical studies that provide insights about the population dynamics of P. x. xanthopus. Each of these studies captures environmental, demographic and behavioural process acting on the population at different scales. These include the analysis of relative abundance data derived from an aerial census, mark recapture sampling of demographic parameters in relation to rainfall patterns and a collection of habitat models derived at different scales using presence-absence data. Spatially explicit PVAs enable the population dynamics of a species to be modelled in space and time. Using these data, a PVA is conducted to explore and rank the importance of the factors that threaten this species and help guide their future monitoring and management. Movement is also a key issue when considering problems such as isolation and inbreeding. Given that little is known about the dispersal behaviour of this species, a range of different dispersal behaviours are also simulated in the PVA using random and non-random mating algorithms, to estimate the potential for inbreeding. / Thesis (PhD)--University of South Australia, 2004.