https://www.selleckchem.com/products/Rolipram.html Reducing displacement and using forests are important strategies to reduce body heat loss and the energetic costs of thermoregulation. However, decreasing movement can limit food access and, consequently, fitness. Therefore, we highlight the importance of forests as thermal shelters. With increasing frequency and intensity of extreme weather events, we showed the need to preserve forest patches to offer suitable conditions for tropical mammals' behavioral thermoregulation. In this context, policies favoring deforestation on Brazilian territory are especially worrisome. Finally, we emphasize the need of integrative approaches to understand the complex interactions between organisms and the environment.The 2 × 2 crossover trial uses subjects as their own control to reduce the intersubject variability in the treatment comparison, and typically requires fewer subjects than a parallel design. The generalized estimating equations (GEE) methodology has been commonly used to analyze incomplete discrete outcomes from crossover trials. We propose a unified approach to the power and sample size determination for the Wald Z-test and t-test from GEE analysis of paired binary, ordinal and count outcomes in crossover trials. The proposed method allows misspecification of the variance and correlation of the outcomes, missing outcomes, and adjustment for the period effect. We demonstrate that misspecification of the working variance and correlation functions leads to no or minimal efficiency loss in GEE analysis of paired outcomes. In general, GEE requires the assumption of missing completely at random. For bivariate binary outcomes, we show by simulation that the GEE estimate is asymptotically unbiased or only minimally biased, and the proposed sample size method is suitable under missing at random (MAR) if the working correlation is correctly specified. The performance of the proposed method is illustrated with several numerical e