Browsing by Subject "Animal Movement"

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    Exploring the Hidden Drivers of Animal Movement: Traits, Disease, and Memory
    (2024-08) Kim, Dongmin
    Movement is one of the most fundamental components of an animal’s survival. Almost all organisms depart from the location of their birth and move across landscapes. In the 19th century, naturalists were fascinated by animal movements and documented variations they observed in animal movement (Darwin. 1906; Wallace. 1877). Humboldt and Ali observed seasonal migration of birds and Marais documented foraging patterns of termites (Humboldt & Bonpland. 1853; Ali. 1996; Marais. 1937). In the early 20th century, ecologists started developing key theories to understand the effects of resources on animal movement. Among many theories, optimal foraging theory introduced the idea that animals foraged to maximize their fitness, which indicated that foragers preferred resources that offered greater energy return (MacArthur & Pianka. 1966). Ideal free distribution theory posited that animal distributions depended on the spatial distribution of habitat quality and resources (Fretwell & Lucas. 1970). Ecologists also wondered why some animals restricted their movements to form a territory like wolves. This led to several theories such as the central place foraging theory (Orians & Pearson. 1979), and optimal foraging theory (Charnov. 1976), where territorial individuals considered the level of resources (whether resources are available in that patch) and the costs and benefits of moving to forage (whether is it worth of energy to travel longer to seek available resources). In the 20th century, live traps were introduced for applied ecologists to estimate the spatial areas used by animals and ecologists soon realized that some animals formed an area that they used regularly without showing territorial behaviors. This finding led to the idea of an animal’s “home range” (Burt. 1943). Methods for estimating an animal's home range continued to evolve and motivated ecologists to explore animal habitat selection, such as resource selection functions, which calculated animal habitat preferences within the range of limited used and available areas that animals could occupy (Manly. 1974; Johnson. 1980). Besides theories related to foraging, ecologists developed many theories addressing different movement types such as dispersal and migration. Earlier ornithologists were interested in the causes of the seasonal migration of birds (Cooke. 1905), and some ecologists developed mathematical models to understand the role of dispersal and population spread (Elton. 1942; Skellam. 1951). In the 21st century, advanced tracking devices have allowed ecologists to record animal movements, including large-scale movements such as seasonal migrations. The ability to document animal movements and to tag multiple individuals has further advanced methods for understanding the effects of habitats and resources on animal movements (Nathan et al., 2008). Despite the continued development of tracking devices, the focus of movement ecology research has been on understanding the effects of habitat quality and resources on animal movement (Holyoak et al., 2008). However, factors that influence animal movement occur in various ways and the effects of resources and habitats are only one component of animal movement. Other external factors (such as individual interactions) and internal factors (such as traits and cognitions) contribute to animal movement behavior. For example, a group of olive baboons (Papio anubis) shared information to make decisions about where to go (Strandburg-Peshkin et al., 2015). Migratory animals like white storks (Ciconia ciconia) developed migratory behaviors by learning as they transitioned from early life to adulthood (Aikens et al., 2024). To date, we still need to learn more about the role of different factors that shape animal movements, especially internal factors that mediate the link between habitat quality and animal movements like cognition and disease. Internal states such as traits, disease infection, and memories motivate individuals to move. For example, recent studies in movement ecology have shown that inherent differences in individual traits are central to variation in animal movement (Bredeweg et al., 2019; Baxter-Gilbert et al., 2019). Animals are born with inherent phenotypic traits that, combined with their experience and development, influence their behavior in response to a given environmental stimulus (Delgado et al., 2010). Some animals can also modify their behavior in response to experiences throughout their lives through perception and memory (Fagan et al., 2013; Lewis et al., 2021). In disease ecology, infected hosts may change their movements based on the progress of their infection (Bradley & Altizer. 2005; Binning et al., 2017). Despite the critical role of internal states in animal movement, relatively little work (an average of 9% of all movement ecology studies between 1997 and 2018) has been done to understand the underlying drivers of movement behavior that cannot be fully understood through external factors such as environmental changes (Holyoak et al., 2008; Joo et al., 2022). For example, do animals navigate where to go based on both their memories and what they perceive by visiting attractive resources? This may be due to the difficulty of observing the effects of the internal factors on animal movement in nature, as opposed to external factors like environmental changes and individual interactions, which can be captured using advanced technologies. Disentangling the internal factors that influence animal movement and understanding the relative contribution of these sources to movement variation is still challenging. My dissertation research is motivated by these challenges to develop models that explore the potential effects of internal factors that influence animal movement behavior in various contexts. My dissertation research considers several internal factors that potentially drive variation in animal movement such as traits, disease, and memory. I then extend the research to explore the impacts of those sources of variability on movement variation within and among individuals. I have applied these steps by constructing different sets of models: both statistical and mathematical. Thanks to high-throughput wildlife technologies, it is now possible to develop empirical movement models and test hypotheses related to the causes of animal movement variation using field observations. The benefit of developing statistical models is that I can conclude whether a particular factor is important to animal movement variation given what I observe in nature (data). Alternatively, I have developed theoretical/mathematical models to help identify optimal behavior given specific conditions. A mathematical model can further ask “what if” questions to explore whether certain mechanisms might be responsible for certain movement patterns we see in nature (e.g., if a condition is true, what will we see in nature). Chapter 1 focuses on movement and disease to understand how parasite transmission and infection tolerance can influence the host’s migratory behavior. Most theoretical models developed to explore migration-infection interactions assume migration is the only trait available to organisms for dealing with a parasite infection, that is, they migrate to a different environment to recover or escape from infection. Yet, it is clear that the presence of other inheritable defense traits in individuals (e.g., tolerance and resistance) can play a critical role in surviving disease infection. Thus, I developed a theoretical model of a host population infected by parasites with two possible traits (tolerance and migration) to reduce the infection cost. The model was constructed using a set of linked differential equations describing the population dynamics of a host infected by a parasite, and I used two different sets of simulations from the model to show how tolerance shapes the host migration strategies. Some, but not all hosts migrated, resulting in what is known as a partial migration. Further, I showed that increasing the cost and benefit of tolerance can both increase and decrease the fraction of the population migrating. The results of the simulations prove that multiple defense traits can interact in conflicting ways to shape a population that migrates in response to infection. In Chapter 2, I lead a review of available statistical methods developed for exploring how memory (i.e., some notion of familiarity with the landscape or experience) influences animal movements. I have partnered with leading movement ecologists at several institutions (University of Minnesota, University of Victoria, University of Wyoming, Biodiversity Pathways Ltd, and Federal University of Mato Grosso do Sul) who together have collected a wealth of tracking data on several wildlife species in North America, including brown bears, mule deer, feral hogs, and sandhill cranes. Numerous methods for incorporating memory into animal movement models have been developed over the past 10 years, offering new opportunities to understand the interactions between memory and movement. I demonstrate how different models can be fit to wildlife tracking data using a series of empirical examples with annotated code templates so that other researchers can easily apply them to their own data. The review also highlights the challenges and limitations of current methods and provides a roadmap for constructing more realistic models that capture the effects of memory on animal movement. Chapter 3 demonstrates the application of hidden Markov models (HMMs) to animal movement data to infer infection status. An understanding of wildlife disease dynamics is important for managing wildlife populations and for quantifying the potential risk of spillover to domestic animals and humans, yet it is difficult to collect data on the infection status of wild, free-ranging animals. Pathogen and parasite infections alter host movement behavior, suggesting that it may be possible to infer infection status from observations of animal movement. We applied hidden Markov models (HMMs) with state-dependent observations of step lengths and turn angles to telemetry data from 68 wild scimitar-horned oryx (Oryx dammah) with known infection status at the time of release for 41 individuals and with information on known deaths. We evaluated the HMMs’ ability to infer infection states using simulated data and veterinary diagnostic reports. The model was able to infer the correct states with a high probability (F1 scores of 0.88 to 0.99) when applied to simulated data. The HMM fitted to the empirical data predicted infection states in oryx, with movement decreasing due to infection. Oryx tended to transition more often from exploring to resting in areas of greater shrub cover after individuals became infected. We encourage other ecologists to consider HMMs as a potential tool for inferring infection-related behavioral states from animal location data.
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    Species-Habitat Associations: Spatial data, predictive models, and ecological insights, 2nd Edition
    (University of Minnesota Libraries Publishing, 2023-01) Matthiopoulos, Jason; Fieberg, John R; Aarts, Geert
    Ecologists develop species-habitat association (SHA) models to understand where species occur, why they are there and where else they might be. This knowledge can be used to designate protected areas, estimate anthropogenic impacts on living organisms and assess risks from invasive species or disease spill-over from wildlife to humans. Here, we describe the state of the art in SHA models, looking beyond the apparent correlations between the positions of organisms and their local environment. We highlight the importance of ecological mechanisms, synthesize diverse modelling frameworks and motivate the development of new analytical methods. Above all, we aim to be synthetic, bringing together several of the apparently disconnected pieces of ecological theory, taxonomy, spatiotemporal scales, and mathematical and statistical technique in our field. The first edition of this ebook reviews the ecology of species-habitat associations, the mechanistic interpretation of existing empirical models and their shared statistical foundations that can help us draw scientific insights from field data. It will be of interest to graduate students and professionals looking for an introduction to the ecological and statistical literature of SHAs, practitioners seeking to analyse their data on animal movements or species distributions and quantitative ecologists looking to contribute new methods addressing the limitations of the current incarnations of SHA models.