Seasonality in Wildlife Populations Across Different Data Resolutions

Abstract

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 low-resolution occurrence data of eight large-mammal species from a camera-trap survey in northwestern Anatolia, Turkiye; mid-resolution count data of Aldabra giant tortoises from transect surveys in the Aldabra Atoll, Seychelles; and high-resolution individual data of gray mouse lemurs in Kirindy Forest, western Madagascar. I analyzed these data with different modeling frameworks tailored to the respective resolution: occupancy models for the low resolution occurrence data, temporary emigration models for the mid-resolution count data, and capture-recapture models for the high-resolution individual-based data. These models assessed system-specific seasonal patterns in changing ecosystems, while providing robust and repeatable analyses.
Chapter 2 uses low-resolution seasonal occurrence data to investigate the seasonal habitat-use patterns of eight large-mammal species in a human-dominated ecosystem in northwestern Turkiye. I show that although all species exhibited seasonal variation in habitat use, the strength of these variations differs among species based on their life histories. Additionally, I demonstrate that these differences are heavily driven by resource availability and human presence in the area. My findings underscore the need to account for variations in species-specific seasonal habitat-use patterns, emphasizing the potential direct and indirect fitness consequences in conservation planning.
Chapter 3 uses the same seasonal occurrence data on large mammals from the previous chapter to explore the seasonal co-occurrence patterns of seven different predator-prey pairs through a multispecies approach. Consistent with the findings of Chapter 2, I show differences in the strength of seasonality in co-occurrences among different pairs. Furthermore, my findings indicate that predatorsâ seasonal habitat use is influenced by seasonally fluctuating prey availability, which varies depending on the species of prey. These findings highlight the importance of a multispecies approach to avoid biased population parameters when investigating seasonality effects in a community.
Chapter 4, which is based on the work of a masters student supervised by me, uses mid-resolution count data to assess the seasonal habitat-use pattern of the Aldabra giant tortoise, a species endemic to the Aldabra Atoll. We find significant variations in the tortoises seasonal habitat use that are driven by fluctuating resources across habitat types. We highlight that these variations have crucial impacts on the tortoises movement and abundance on the atoll. As such, our findings provide valuable information on possible responses and coping mechanisms of this species to changing environmental conditions. Given the increasing threat of climate change on the atoll, this information is crucial for conservation and habitat management.
Chapter 5 uses high-resolution individual data of gray mouse lemurs from Kirindy Forest, Madagascar, to assess seasonal patterns in a detailed demographic parameter, survival. I show significant seasonal variation in the lemurs survival, with distinctions between the sexes due to sex-specific survival strategies. Moreover, my findings indicate that density dependence and maximum temperature during the dry season are primary drivers in the seasonal survival patterns. As climate change poses challenges to Kirindy Forest, my research reveals important seasonal demographic patterns from different lemur groups in the population, emphasizing their impact on population persistence. In conclusion, my thesis provides critical empirical insights into the effects of seasonality on diverse demographic parameters across wildlife populations, while addressing the current challenges in determining seasonality patterns. It introduces robust methodological frameworks to assess seasonality effects using available data of different resolutions; and it offers improvements in different methodological aspects. Given the escalating pressure of climate change, accurately assessing seasonality effects on wildlife becomes increasingly important. Tailoring population models to existing data enables understanding current situations of wildlife populations and predicting their future adaptations and responses. These aspects are essential for effective wildlife management and successful conservation.