The age structure of populations is an important and often unresolved factor in ecology and wildlife management. Parameters like onset of reproduction and senescence, reproductive success and survival rate are tightly correlated with age. Unfortunately, age information of wild animals is not easy to obtain, especially for birds, where few anatomical markers of age exist. Longitudinal age data from birds banded as chicks are rare, particularly in long lived species. Age estimation in such species would be extremely useful as their long life span typically indicates slow population growth and potentially the need for protection and conservation. Telomere length change has been suggested as a universal marker for ageing vertebrates and potentially other animals. This method, termed molecular ageing, is based on a shortening of telomeres with each cell division. In birds, the telomere length of erythrocytes has been reported to decline with age, as the founder cells (haematopoietic stem cells) divide to renew circulating red blood cells. I measured telomere length in kakapo, the world largest parrot and four other bird species (Buller’s albatross, kea, New Zealand robin and saddleback) using telomere restriction fragment analysis (TRF) to assess the potential for molecular ageing in these species. After providing an overview of methods to measure telomere length, I describe how one of them (TRF) measures telomere length by quantifying the size distribution of terminal restriction fragments using southern blot of in-gel hybridization (Chapter 2). Although TRF is currently the ‘gold standard’ to measure telomere length, it suffers from various technical problems that can compromise precision and accuracy of telomere length estimation. In addition, there are many variations of the protocol, complicating comparisons between publications. I focused on TRF analysis using a non-radioactive probe, because it does not require special precautions associated with handling and disposing of radioactive material and therefore is more suitable for ecology laboratories that typically do not have a strong molecular biology infrastructure. However, most of my findings can be applied to both, radioactive and nonradioactive TRF variants. I tested how sample storage, choice of restriction enzyme, gel Abstract II electrophoresis and choice of hybridization buffer can influence the results. Finally, I show how image analysis (e.g. background correction, gel calibration, formula to calculate telomere length and the analysis window) can not only change the magnitude of estimated telomere length, but also their correlation to each other. Based on these findings, I present and discuss an extensive list of methodological difficulties associated with TRF and present a protocol to obtain reliable and reproducible results. Using this optimized protocol, I then measured telomere length of 68 kakapo (Chapter 3). Almost half of the current kakapo population consists of birds that were captured as adults, hence only their minimum age is known (i.e. time from when they were found +5 years to reach adulthood). Although molecular ageing might not be able to predict chronological age accurately, as calibrated with minimum age of some birds, it should be able to compare relative age between birds. Recently, the oldest kakapo (Richard Henry) was found to show signs of reproductive senescence. The age (or telomere length) difference to Richard Henry could have been used to approximate the remaining reproductive time span for other birds. Unfortunately, there was no change of telomere length with age in cross sectional and longitudinal samples. Analysis of fitness data available for kakapo yielded correlations between telomere length and fledging success, but they were weak and disappeared when the most influential bird was excluded from analysis. The heavy management and small numbers of kakapo make conclusions about fitness and telomere length difficult and highly speculative. However, telomere length of mothers and their chicks were significantly correlated, a phenomena not previously observed in any bird. To test if the lack of telomere loss with age is specific to kakapo, I measured telomere length of one of its closest relatives: the kea (Chapter 4). Like kakapo, telomere length did not show any correlation with age. I then further assessed the usefulness of molecular ageing in birds using only chicks and very old birds to estimate the maximum TL range in an additional long lived (Buller’s albatross) and two shorter lived species (NZ robin and saddleback). In these Abstract III species, telomere length was on average higher in chicks than in adults. However, age matched individuals showed high variations in telomere length, such that age dependent and independent telomere length could not be distinguished. These data and published results from other bird species, coupled with the limitations of methodology I have identified (Chapter 2), indicate that molecular ageing does not work in most (if not all) birds in its current suggested form. Another way to measure telomere length is telomere Q-PCR, a real-time PCR based method. Measurement of the same kakapo samples with TRF and Q-PCR did not result in comparable results (Chapter 4). Through experimentation I found that differences in amplification efficiency between samples lead to unreliable estimation of telomere length using telomere Q-PCR. These differences were caused by inhibitors present in the samples. The problem of differential amplification efficiency in Q-PCR, while known, is largely ignored by the scientific community. Although some methods have been suggested to correct for differing efficiency, most of these introduce more error than they eliminate. I developed and applied an assay based on internal standard oligonucleotides that was able to corrected EDTA induced quantification errors of up to 70% with high precision and accuracy (Chapter 5). The method, however, failed when tested with other inhibitors commonly found in DNA samples extracted from blood (i.e. SDS, heparin, urea and FeCl3). PCR inhibition was highly selective in the probe-polymerase system I used, inhibiting amplification of genomic DNA, but not amplification of internal oligonucleotide or plasmid standards in the same reaction. Internal standards are a key feature of most diagnostic PCR assays to identify false negatives arising from amplification inhibition. The differential response to inhibition I identified greatly compromises the accuracy of these assays. Consequently, I strongly recommend that researchers using PCR assays with internal standards should verify that the target DNA and internal standard actually respond similarly to common inhibitors.

The age structure of populations is an important and often unresolved factor in ecology and wildlife management. Parameters like onset of reproduction and senescence, reproductive success and survival rate are tightly correlated with age. Unfortunately, age information of wild animals is not easy to obtain, especially for birds, where few anatomical markers of age exist. Longitudinal age data from birds banded as chicks are rare, particularly in long lived species. Age estimation in such species would be extremely useful as their long life span typically indicates slow population growth and potentially the need for protection and conservation. Telomere length change has been suggested as a universal marker for ageing vertebrates and potentially other animals. This method, termed molecular ageing, is based on a shortening of telomeres with each cell division. In birds, the telomere length of erythrocytes has been reported to decline with age, as the founder cells (haematopoietic stem cells) divide to renew circulating red blood cells. I measured telomere length in kakapo, the world largest parrot and four other bird species (Buller’s albatross, kea, New Zealand robin and saddleback) using telomere restriction fragment analysis (TRF) to assess the potential for molecular ageing in these species. After providing an overview of methods to measure telomere length, I describe how one of them (TRF) measures telomere length by quantifying the size distribution of terminal restriction fragments using southern blot of in-gel hybridization (Chapter 2). Although TRF is currently the ‘gold standard’ to measure telomere length, it suffers from various technical problems that can compromise precision and accuracy of telomere length estimation. In addition, there are many variations of the protocol, complicating comparisons between publications. I focused on TRF analysis using a non-radioactive probe, because it does not require special precautions associated with handling and disposing of radioactive material and therefore is more suitable for ecology laboratories that typically do not have a strong molecular biology infrastructure. However, most of my findings can be applied to both, radioactive and nonradioactive TRF variants. I tested how sample storage, choice of restriction enzyme, gel Abstract II electrophoresis and choice of hybridization buffer can influence the results. Finally, I show how image analysis (e.g. background correction, gel calibration, formula to calculate telomere length and the analysis window) can not only change the magnitude of estimated telomere length, but also their correlation to each other. Based on these findings, I present and discuss an extensive list of methodological difficulties associated with TRF and present a protocol to obtain reliable and reproducible results. Using this optimized protocol, I then measured telomere length of 68 kakapo (Chapter 3). Almost half of the current kakapo population consists of birds that were captured as adults, hence only their minimum age is known (i.e. time from when they were found +5 years to reach adulthood). Although molecular ageing might not be able to predict chronological age accurately, as calibrated with minimum age of some birds, it should be able to compare relative age between birds. Recently, the oldest kakapo (Richard Henry) was found to show signs of reproductive senescence. The age (or telomere length) difference to Richard Henry could have been used to approximate the remaining reproductive time span for other birds. Unfortunately, there was no change of telomere length with age in cross sectional and longitudinal samples. Analysis of fitness data available for kakapo yielded correlations between telomere length and fledging success, but they were weak and disappeared when the most influential bird was excluded from analysis. The heavy management and small numbers of kakapo make conclusions about fitness and telomere length difficult and highly speculative. However, telomere length of mothers and their chicks were significantly correlated, a phenomena not previously observed in any bird. To test if the lack of telomere loss with age is specific to kakapo, I measured telomere length of one of its closest relatives: the kea (Chapter 4). Like kakapo, telomere length did not show any correlation with age. I then further assessed the usefulness of molecular ageing in birds using only chicks and very old birds to estimate the maximum TL range in an additional long lived (Buller’s albatross) and two shorter lived species (NZ robin and saddleback). In these Abstract III species, telomere length was on average higher in chicks than in adults. However, age matched individuals showed high variations in telomere length, such that age dependent and independent telomere length could not be distinguished. These data and published results from other bird species, coupled with the limitations of methodology I have identified (Chapter 2), indicate that molecular ageing does not work in most (if not all) birds in its current suggested form. Another way to measure telomere length is telomere Q-PCR, a real-time PCR based method. Measurement of the same kakapo samples with TRF and Q-PCR did not result in comparable results (Chapter 4). Through experimentation I found that differences in amplification efficiency between samples lead to unreliable estimation of telomere length using telomere Q-PCR. These differences were caused by inhibitors present in the samples. The problem of differential amplification efficiency in Q-PCR, while known, is largely ignored by the scientific community. Although some methods have been suggested to correct for differing efficiency, most of these introduce more error than they eliminate. I developed and applied an assay based on internal standard oligonucleotides that was able to corrected EDTA induced quantification errors of up to 70% with high precision and accuracy (Chapter 5). The method, however, failed when tested with other inhibitors commonly found in DNA samples extracted from blood (i.e. SDS, heparin, urea and FeCl3). PCR inhibition was highly selective in the probe-polymerase system I used, inhibiting amplification of genomic DNA, but not amplification of internal oligonucleotide or plasmid standards in the same reaction. Internal standards are a key feature of most diagnostic PCR assays to identify false negatives arising from amplification inhibition. The differential response to inhibition I identified greatly compromises the accuracy of these assays. Consequently, I strongly recommend that researchers using PCR assays with internal standards should verify that the target DNA and internal standard actually respond similarly to common inhibitors.

Understanding the relationship between organisms and their environment is particularly important for the conservation and management of endangered species. The kakapo (Strigops habroptilus, Gray 1845) is a critically endangered, lek breeding, flightless nocturnal parrot endemic to New Zealand. In April 1998, a total population of fifty-six kakapo was known to survive on offshore islands. Twenty-six kakapo, thirteen males and thirteen females, were temporarily transferred to Pearl Island (518 ha), southern Stewart Island, from April 1998 to April 1999. The translocation of kakapo to Pearl Island, and subsequent breeding season, provided an ideal experimental framework to study kakapo dispersal, movement patterns, home range development, habitat selection, and lek development during the non-breeding and breeding seasons. A total of 4425 radio locations were analysed for all twenty-six birds, with a mean error polygon of 0.03 ha and an estimated average radio telemetry error of 21.6 m. Various home range analysis techniques were used to estimate kakapo home range size and overlap including: minimum convex polygons (MCP), modified minimum convex polygons (MMCP), harmonic mean analysis, adaptive kernel methods and cluster analysis. Estimates of kakapo home range size differed significantly depending on the method used (ANOVA, general linear model: F₁₃, ₁₀₇₆ = 63.99, p < 0.0001) and the season (F₂, ₁₀₇₆ = 160.75, p < 0.0001). Breeding home range size was significantly larger than non-breeding range size (mean difference = 67.6 ha, t₂₅ = 15.27, p < 0.0001). Calculations from 100% MCP and 95% harmonic mean analysis resulted in larger estimates of home range size and overlap compared to other methods. Cluster and kernel analyses appeared to give the most accurate home range representation for kakapo. Core home range areas showed a greater degree of similarity between methods. Male and female mean annual home range size did not differ significantly, whereas males had significantly (p < 0.05) larger home ranges than females during the nonbreeding season. Minimum convex polygons and harmonic mean analysis suggested that there was no significant difference in the way in which males and females interacted with each other. Kernel and cluster analyses indicated that females would overlap a greater proportion of another bird�s home range than males would. Cluster analysis also indicated that a female would have more of her home range occupied by another bird than a male would. The fact that different methods produced different quantitative results is an important consideration when using home range analysis to make conservation management decisions. Researchers must determine which method is the most appropriate for a particular research objective, species, or study area. The application of geographical information systems, ERDAS image classification techniques and global positioning systems was an integral part of this study. A large-scale vegetation classification map of Pearl Island was produced in order to quantify habitat selection by kakapo. The unsupervised classification technique produced the least accurate vegetation map, with an accuracy measure of 17-23%, compared to 52% for the supervised classification. The highest accuracy was obtained using an integrated approach involving inductive classification and deductive mapping, resulting in a vegetation classification map which correctly classified 95% of vegetation samples. Thirty-seven ecotone classes were identified and a total ecotone length of approximately 124 km was detected. Resource selection ratios and resource selection functions were estimated using a combination of discrete, continuous and area-based habitat variables. Circular buffers around used and available point locations were generated to determine whether kakapo selectively use vegetation mosaics. The probability of selection increased with increasing species diversity in each 75-metre radius buffer. Kakapo selected habitat mosaics and vegetation types with higher species diversity and moderate to high abundance of mature rimu and yellow silver pine trees.

Understanding the relationship between organisms and their environment is particularly important for the conservation and management of endangered species. The kakapo (Strigops habroptilus, Gray 1845) is a critically endangered, lek breeding, flightless nocturnal parrot endemic to New Zealand. In April 1998, a total population of fifty-six kakapo was known to survive on offshore islands. Twenty-six kakapo, thirteen males and thirteen females, were temporarily transferred to Pearl Island (518 ha), southern Stewart Island, from April 1998 to April 1999. The translocation of kakapo to Pearl Island, and subsequent breeding season, provided an ideal experimental framework to study kakapo dispersal, movement patterns, home range development, habitat selection, and lek development during the non-breeding and breeding seasons. A total of 4425 radio locations were analysed for all twenty-six birds, with a mean error polygon of 0.03 ha and an estimated average radio telemetry error of 21.6 m. Various home range analysis techniques were used to estimate kakapo home range size and overlap including: minimum convex polygons (MCP), modified minimum convex polygons (MMCP), harmonic mean analysis, adaptive kernel methods and cluster analysis. Estimates of kakapo home range size differed significantly depending on the method used (ANOVA, general linear model: F₁₃, ₁₀₇₆ = 63.99, p < 0.0001) and the season (F₂, ₁₀₇₆ = 160.75, p < 0.0001). Breeding home range size was significantly larger than non-breeding range size (mean difference = 67.6 ha, t₂₅ = 15.27, p < 0.0001). Calculations from 100% MCP and 95% harmonic mean analysis resulted in larger estimates of home range size and overlap compared to other methods. Cluster and kernel analyses appeared to give the most accurate home range representation for kakapo. Core home range areas showed a greater degree of similarity between methods. Male and female mean annual home range size did not differ significantly, whereas males had significantly (p < 0.05) larger home ranges than females during the nonbreeding season. Minimum convex polygons and harmonic mean analysis suggested that there was no significant difference in the way in which males and females interacted with each other. Kernel and cluster analyses indicated that females would overlap a greater proportion of another bird�s home range than males would. Cluster analysis also indicated that a female would have more of her home range occupied by another bird than a male would. The fact that different methods produced different quantitative results is an important consideration when using home range analysis to make conservation management decisions. Researchers must determine which method is the most appropriate for a particular research objective, species, or study area. The application of geographical information systems, ERDAS image classification techniques and global positioning systems was an integral part of this study. A large-scale vegetation classification map of Pearl Island was produced in order to quantify habitat selection by kakapo. The unsupervised classification technique produced the least accurate vegetation map, with an accuracy measure of 17-23%, compared to 52% for the supervised classification. The highest accuracy was obtained using an integrated approach involving inductive classification and deductive mapping, resulting in a vegetation classification map which correctly classified 95% of vegetation samples. Thirty-seven ecotone classes were identified and a total ecotone length of approximately 124 km was detected. Resource selection ratios and resource selection functions were estimated using a combination of discrete, continuous and area-based habitat variables. Circular buffers around used and available point locations were generated to determine whether kakapo selectively use vegetation mosaics. The probability of selection increased with increasing species diversity in each 75-metre radius buffer. Kakapo selected habitat mosaics and vegetation types with higher species diversity and moderate to high abundance of mature rimu and yellow silver pine trees.

Kakapo (Strigops habroptilus) are a flightless, nocturnal parrot endemic to New Zealand. Thought to be extinct within their natural range, kakapo are currently listed as nationally critical. The current population of 86 individuals is managed by the Department of Conservation’s National Kakapo Team on two offshore islands in southern New Zealand, with all females of breeding age on Codfish Island (Whenua Hou). Kakapo only breed once every two to five years, coinciding with the mast fruiting of specific plant species. On Codfish Island, the proportion of adult female kakapo that breed in rimu (Dacrydium cupressinum) fruiting years is dependent on the quantity of fruit produced, with fewer females attempting to breed during low mast years. The purpose of this research is to investigate why only some adult female kakapo breed in low rimu fruiting years on Codfish Island, specifically assessing if foraging home range size and/or habitat selection influence breeding. A total of 506 location points were collected at night for 18 adult female kakapo between March and May 2006. These were used to estimate foraging home ranges and to assess if kakapo select for particular types of vegetation. Ecological Niche Factor Analysis was used to determine the relative importance of habitat variables in the distribution of female kakapo and to predict areas of suitable breeding habitat when rimu fruit is limited. The breeding success of individuals in 2005, a low rimu mast year, was used to identify if differences in home ranges or habitat selection occurred between breeding and non-breeding females. The large variation in foraging home range sizes recorded in this research was consistent with previous studies. Foraging home range sizes were on average twice the size for breeders than for non-breeders, suggesting that adult female kakapo may be limited in their ability to breed by the size of the area they occupy. Adult female kakapo did not randomly use vegetation on Codfish Island as some vegetation types were not used, while others were common inside foraging home ranges. Adult female kakapo utilise a broad niche and are capable of surviving in a wide range of habitats. However, breeding females were more specialised in their niche requirements than non-breeders, with breeders utilising areas with higher abundances of mature rimu trees. Females occurred in high elevation, flat areas of the island but this may have been because this is where appropriate vegetation types occurred. During low rimu mast years, breeding adult females were predicted to occupy habitat in high elevation, plateau areas with a high abundance of rimu. Areas identified as sub-optimal habitat for breeding included the coastal areas, the lower elevation area of the main valley and some ridgelines. The home ranges of all 10 breeding females contained some optimal habitat, while females who did not breed were more likely to be located in sub-optimal habitat. Although there were significant areas of optimal breeding habitat not occupied by adult female kakapo, other kakapo may have been present in these areas. To increase the proportion of females that breed in low rimu mast years, it may be necessary to remove sub-adult females or surplus adult males living in optimal breeding habitat from the island. Alternatively, females in sub-optimal breeding habitat could be fed supplementary foods or transferred to other islands where there is unoccupied suitable breeding habitat available.