No abstract

Temperature implies contrasting biological causes of demographic aging in poikilotherms. In this work, we used the reliability theory to describe the consistency of mortality with age in moth populations and to show that differentiation in hazard rates is related to extrinsic environmental causes such as temperature. Moreover, experiments that manipulate extrinsic mortality were used to distinguish temperature-related death rates and the pertinence of the Weibull aging model. The Newton-Raphson optimization method was applied to calculate parameters for small samples of ages at death by estimating the maximum likelihoods surfaces using scored gradient vectors and the Hessian matrix. The study reveals for the first time that the Weibull function is able to describe contrasting biological causes of demographic aging for moth populations maintained at different temperature regimes. We demonstrate that at favourable conditions the insect death rate accelerates as age advances, in contrast to the extreme temperatures in which each individual drifts toward death in a linear fashion and has a constant chance of passing away. Moreover, slope of hazard rates shifts towards a constant initial rate which is a pattern demonstrated by systems which are not wearing out (e.g. non-aging) since the failure, or death, is a random event independent of time. This finding may appear surprising, because, traditionally, it was mostly thought as rule that in aging population force of mortality increases exponentially until all individuals have died. Moreover, in relation to other studies, we have not observed any typical decelerating aging patterns at late life (mortality leveling-off), but rather, accelerated hazard rates at optimum temperatures and a stabilized increase at the extremes.In most cases, the increase in aging-related mortality was simulated reasonably well according to the Weibull survivorship model that is applied. Moreover, semi log- probability hazard rate model illustrations and maximum likelihoods may be usefully in defining periods of mortality leveling off and provide clear evidence that environmental variability may affect parameter estimates and insect population failure rate. From a reliability theory standpoint, failure rates vary according to a linear function of age at the extremes indicating that the life system (i.e., population) is able to eliminate earlier failure and/or to keep later failure rates constant. The applied model was able to identify the major correlates of extended longevity and to suggest new ideas for using demographic concepts in both basic and applied population biology and aging.

In this study, a probabilistic degree‐day phenology model has been developed for the codling moth, Cydia pomonella, and calibrated using data from laboratory growth studies. The model is further used to predict the succession and overlapping of certain biological events of C. pomonella in probabilistic‐physiological time scale in northern Greece fruit orchards. The model satisfactorily predicts the stage‐specific pest population dynamics, including egg laying and hatching, the occurrence of larvae and pupae stages and the emergence of adults. According to the model projections for the adult flights, there is a very high probability, p = 0.999, of observing adults of the first flight generation until 333 degree‐days (DD), but a very low probability of finding adults of the second flight generation. Moreover, at 575 DD, the probability of finding an individual to lay eggs is p = 0.15. However, there is nearly the same probability of egg hatch, p = 0.36, and larval completion p = 0.313, while at the same time, the probability of pupal completion is very low, p = 0.001. The above model predictions were validated using field data for the adult stage emergence as well as for the percentage of larval damage providing satisfactory results considering that larval emergence prediction was close to actual fruit damage observed in field. This information is very important considering that IPM programs rely on the use of biorational compounds, such as IGRs and bio‐toxins which are stage selective and often have a shorter residual activity than the preceding broad‐spectrum insecticides.

In this work we address the question of how certain climatic variables may be significant related to alterations of avian biodiversity in a semi-agricultural Natura wetland side in Northern Greece. In particular, we examine the interplay between temperature, relative humidity and three different bird biodiversity indexes, including Shannon Entropy, Simpson's dominance (evenness) index and the Berger-Parker index. By using different modeling approaches, parametric and nonparametric multivariate models, we make effort to get a consensus on the interrelationships between climate and avian biodiversity. In particular, we show that in most cases nonlinear models and surface-plot analysis methodology, are able to capture the relation of a considerable increase in the estimated biodiversity indexes with increased temperatures and rain levels. Thus, biodiversity is to a significant extent affected by the aforementioned climate factors at a proximate level involving synergies between the different climate factors. Revealing potential interrelationship between biodiversity and climate drivers although is a complex-even though challenging-task, contributing to our understanding of the mechanisms connecting climate change with ecosystem functioning. Moreover, a better understanding of biodiversity functioning in relation to climate is essential for biodiversity awareness and the design of effective biodiversity-related conservation management policies.

In this study we explore for the first time the combined effect of climate variables and remote sensing land cover indicators on bird richness in a representative wetland location of the Thermaikos gulf Natura 2000 protected area in Northern Greece. In particular the association between bird diversity and climate, as well as remote sensed land cover indices was explored for seven successive seasons using correlation analysis and a Cox-Box transformed multivariate linear model. Three climate variables were tested, mean temperature, rain level and mean relative humidity and three land cover indices, the Normalized difference index (NDVI), the atmospheric resistance vegetation index (ARVI) and an agricultural band combination index (ABCI). Among the environmental drivers explored, temperature, rain levels and ABCI were significantly correlated to bird richness in contrast to NDVI and ARVI which showed a lower correlation, while relative humidity displayed the poorest correlation. Thus, temperature, rain levels and agricultural intensity significantly influenced bird composition. Additionally, the multivariable linear model indicates that temperature, rain levels and ABCI have a statistically significant effect (p<0.05) on bird species richness accounting for 73,02% of data variability. Based on the overall model results and the related 3D contour plot simulation we can conclude that in general, bird species richness increases with an increase in temperature and Rain, as well as with a decrease in agricultural intensity (ABCI). Understanding the factors that can affect bird biodiversity of species is of great importance in ecosystem management and species conservation.