Seasonality in Wildlife Populations Across Different Data Resolutions

Seasonality is an important phenomenon that causes temporal patterns in environmental factors and variations therein, which in turn shape ecosystems and demography of species inhabiting them. Seasonality can cause variations in resource availability, environmental and anthropogenic disturbances, and inter- as well as intra-species interactions. These seasonally varying factors can influence species demographic parameters and coping strategies. Identifying how these parameters and strategies are affected by seasonal changes enables a comprehensive understanding of the underlying mechanisms of population persistence; and it can provide evidence-based recommendations for conservation efforts. Such understanding, in turn, improves prediction of future responses to environmental changes and fluctuations in population dynamics, and thereby contributes to more accurate conservation and management plans. However, even though seasonality is widely recognized in population ecology research, due to logistical challenges associated with collecting seasonal data, researchers often rely on annually collected data and thereby overlook effects of environmental seasonality on wildlife populations. This shortcoming can obscure crucial seasonal information and may lead to biased outcomes in population analyses. Depending on monitoring technique, wildlife population analyses are based on data that come in different resolutions, ranging from low-resolution occurrence data to mid-resolution count data and high-resolution individual-based data. Because different data resolutions enable different modeling frameworks, a comprehensive approach to seasonality analysis requires an understanding of how the different data resolutions can be employed to study seasonality effects on wildlife populations. In this thesis, I adopted a multifaceted approach to demonstrate how different data resolutions can be applied to investigate seasonality effects on the demography of diverse species across three distinct systems. For this, I used ...