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Understanding how birds annually allocate energy to cope with changing environmental conditions and physiological states is a fundamental question in avian ecology. The two main hypotheses to explain annual patterns in energy use are "reallocation" and "increased demand". The reallocation hypothesis suggests equal energetic costs in winter and breeding seasons, while the increased demand suggests that energy demand should be highest during breeding. Under the standard aerobic capacity model of endothermy, birds are expected to adjust the mass and/or metabolic intensity of their bodies in ways that are consistent with expected cold- and/or activity-induced costs. Here, we look for metabolic signatures of reallocation versus increased demands in the energy requirements of a small, resident passerine of a temperate-zone (great tit, Parus major). To do so, we measured whole-body and mass-independent basal (BMR), summit (Msum), and field (FMR) metabolic rates during late winter and during the chick-rearing period (breeding). We also assessed whether, and to what extent, metabolic rates conform to the predictions of the aerobic capacity model of endothermy. We found that great tits showed no substantial differences in energy expenditure between winter and the breeding season, providing support for the reallocation hypothesis. Only mass-independent Msum showed seasonal variation, with significantly higher values (~4%) in winter compared to the breeding season. Our results also lend support to the predictions of the aerobic capacity model for the evolution of endothermy, as we found that whole-body BMR and Msum were positively related. We argue that both energy reallocation and the limited increase in mass-independent Msum are consistent with the relatively mild winter temperatures recorded during our study period. Our results confirm that both BMR and Msum are flexible traits that vary in ways that are consistent with expected cold- and/or activity-induced costs.

The numerical increase of roe deer has resulted in a noticeable growth of hunting activities allowing to have a great availability of biometrical data from both the hunted samples and the population by describing trends and variations and, then, optimize the consequent wildlife management decisions. Specifically, we analyzed biometric data of a roe deer (Capreolus capreolus) population at the Macerata Province, in central Italy. We focused on two animal groups: fawns (<11 months old) and adults (>2 years old). We assessed the relationship between body parameters and trophic resources available during the autumn-winter period. In particular, we analyzed live weight, head-trunk length, height at shoulders, chest circumference, length of hock, total length of the cranium, condilo-basal length, mastoid width, zygomatic width, ectorbital width, entorbital width, teeth row length and mandible length. In the context of studying trophic resources, we identified three environmental categories based on their carrying capacity and then assigned a specific environmental category to each sample. Statistical analysis was performed. In fawn, only zygomatic width showed significant differences both between sexes and among the environmental categories. In adult, the analysis showed significant differences between sexes for live weight, head-trunk length, length of hock and zygomatic width, with the highest values for males. Among environmental categories, the analysis showed significant differences for live weight, head-trunk length, length of hock chest circumference, length of hock, ectorbital width and teeth row length. This approach, applied to a broader database could be useful to identify suitable parameters to be used in planning the optimum selective hunting of roe deer.

Study of dimensions (biometry) and shapes (geometric morphometry) of bone structures in ungulates is of extreme importance in wildlife population management. Unlike classical biometry, which involves the use of a caliper for measurements, geometric morphometry acquires, through software, a series of reference points (landmarks) from digital photos, providing a series of linear measures. A method to convert values obtained from the GeoGebra software into biometric measures is described. We took photos of 25 mandibles of adult roe deer and at the same time measured mandible length and teeth row length using a caliper. After image processing using GeoGebra, we calculated the conversion factor as the mean ratio between measures taken using GeoGebra and the caliper. The series of measurements, taken with two different methods (direct measurement using the caliper and conversion from GeoGebra output), showed a good degree of agreement. We used the conversion factor to obtain, from the GeoGebra database, four additional parameters of 50 mandibles. The analysis of variance showed that one parameter was significantly different between sexes (p = 0.04), demonstrating the usefulness of the measurement conversion. The conversion factor is helpful to improve classical biometric databases to better clarify the relationship between environment and wildlife status.