https://ogma.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:15391 Wed 11 Apr 2018 16:58:28 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:15390 Wed 11 Apr 2018 11:35:59 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:15372 Wed 11 Apr 2018 10:19:10 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:32578 Thu 21 Jun 2018 11:53:16 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:12316 Sat 24 Mar 2018 08:11:35 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:27463 Eucalyptus leucoxylon and E. tricarpa) from the Maryborough region of Victoria between 1940 and 1962. Monthly behaviour (start, peak, finish, monthly intensity, duration and success) in budding and flowering was assessed using the indices of Keatley et al. (1999) and Keatley & Hudson (2007). Although E. tricarpa buds are significantly (P < 0.01) positively and linearly related to higher minimum temperature (≥ 9°C) both flowering and buds decrease significantly with maximum temperature (>21°C) (P < 0.01). Models of flowering including current bud status and climate show that E. tricarpa flowering is positively related to current budding intensities (buds > 4.5) (P = 0.0000) and increases with elevated rainfall (from 40 to approximately 88 mm) (P=0.045) (R²=60.8%). Inclusion of current budding as well as budding intensity 1 to 3 months prior to flowering in the models show E. tricarpa’s flowering to significantly decrease and cease above 7.7°C minimum temperature, and increase with increased rainfall between appropriately 44 and 93 mm. Budding 2 months prior is a positive influence (P < 0.007), combined current budding and budding 2 months prior indicate flowering commences within the budding range of 4 to 6 (R²=71.4%). For E. tricarpa minimum temperature is shown to drive increased budding but is associated with decreased flowering. Maximum temperature is associated with both increased budding and increased flowering for E. tricarpa; and flowering increases non-linearly both with elevated rainfall (from 40 -90 mm) and with increased buds. For E. leucoxylon buds are significantly (P < 0.01) negatively and linearly related to elevated maximum temperature (> 23°C) (Z = -3.2, P < 0.0001) and buds increase with increasing minimum temperature ((≥ 9°C) (Z =1.92, P < 0.08, 10% sig). Budding is significantly but nonlinearly influenced by rainfall: rain up to 40 mm has a positive influence and 40 to 80 negative. Models of E. leucoxylon flowering, which include current bud status and climate, show that E. leucoxylon’s flowering is positively and nonlinearly related to current buds (buds > 5.5) (P = 0.000001) and decreases significantly with elevated minimum temperature (≥ 8.5°C) (Z = - 2.38, P < 0.0001) (R² = 42.6%). Inclusion of budding 1 to 3 months in the models show E. leucoxylon flowering to significantly increase with higher current bud quantity (Z = 2.57, P < 0.0001) and nonlinearly with respect to bud quantity 2 months prior (P < 0.005) - with flowering commencing with bud intensity above 4.5 and decreasing when buds reach 7.0 (R²=68.9%). This study has confirmed that for flowering to start, buds must have reached a particular maturity, before flowering occurs. For E. tricarpa this seems to occur when bud intensity has reached greater than 4.5, with a slightly lower value for E. leucoxylon, indicating that this species buds need longer to mature - this in turn further assists in separating the temporal flowering peaks between the two species. Additionally, a maximum flowering intensity is indicated with the inclusion of lagged budding: 6.0 for E. tricarpa and 7.0 for E. leucoxlyon. The inclusion of lagged budding found that budding two months prior was influential on flowering. Noteworthy is that 2 months is the most common period when temperature has the greatest influence on flowering (Hudson and Keatley, 2010a; Hudson et al., 2011a; Hudson et al., 2011c; Menzel and Sparks, 2006). These results indicate that it might not just be temperature, but temperature influencing the development of buds, which in turns influences flowering. This needs further work and the examination of additional species, but given that flowering is dependent on budding, this postulate makes sense (Primack, 1987).]]> Sat 24 Mar 2018 07:32:42 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24593 Sat 24 Mar 2018 07:11:49 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:22977 Sat 24 Mar 2018 07:11:38 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:22979 aggregate association index (or the AAI), developed by Beh (2008 and 2010) which enumerates the overall extent of association about individuals that may exist at the aggregate level when individual level data is not available. The applicability of the technique is demonstrated by using leukaemia relapse data of Cave et al. (1998). This data is presented in the form of a contingency table that cross-classifies the follow up status of leukaemia relapse by whether cancer traces were found (or not) on the basis of polymerase child reaction (PCR) – a modern method used to detect cancerous cells in the body assumed superior than conventional for that period, microscopic identification. Assuming that the joint cell frequencies of this table are not available, and that the only available information is contained in the aggregate data, we first quantify the extent of association that exists between both variables by calculating the AAI. This index shows that the likelihood of association is high. As the AAI has been developed by exploiting Pearson’s chi-squared statistics, the AAI inherently suffers from the well-known large sample size effect that can overshadow the true nature of the association shown in the aggregate data of a given table. However, in this paper we show that the impact of sample size can be isolated by generating a pseudo population of 2x2 tables under the given sample size. Therefore, the focus of this paper is to present an approach to help answer the question “is this high AAI value statistically significant or not?” by using aggregate data only. The answer to this question lies we believe, in the calculation of the p-value of the nominated index. We shall present a new method of numerically quantifying the p-value of the AAI thereby gaining new insights into the statistical significance of the association between two dichotomous variables when only aggregate level information is available. The pseudo p-value approach suggested in this paper enhances the applicability of the AAI and thus can be considered a valuable addition to the literature of aggregate data analysis.]]> Sat 24 Mar 2018 07:11:37 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:32966 Mon 23 Sep 2019 12:37:33 AEST ]]>