The impacts of human activities on the natural world have accelerated rapidly in recent centuries and decades. Consequent loss and fragmentation of natural habitats is the greatest threat to short and long-term survival of the planet’s rich biodiversity. Large carnivores are particularly sensitive to these changes, as many species rely on expansive natural areas to maintain healthy, genetically diverse populations.
As a result, this charismatic clade is a focal point of conservation attention, and is also frequently used as a conservation umbrella to conserve other species which share their broad range of habitats. While diminished, fragmented populations and geographic isolation can be detrimental to species longevity, habitat corridors which connect populations throughout a broader human-dominated landscape provide resistance and resilience to the effects of isolation by maintaining genetic connectivity between sub-populations. Accordingly, understanding how large carnivores move through natural and non-natural landscapes to connect with other populations is a key area of research in movement ecology and conservation biology.
In this dissertation, collaborators and I implemented open-source synthetic, comparative, and machine learning approaches to model the movement of tigers and jaguars, two ecologically vital and connectivity-dependent carnivore species, in regions of their ranges which are largely shared with humans.
For Chapters 1 and 2, focusing on tigers in central India, we synthesized five independently derived layers of landscape resistance to derive consensus among existing research (Chapter 1) and comparatively test different movement simulation techniques’ abilities to predict tiger occurrence data (Chapter 2). We found that existing research efforts on habitat quality and potential connectivity areas for tigers in central India were more aligned than independent results indicated. We also derived a geospatial layer for “consensus connectivity areas (CCAs)” – areas where existing research agreed on high potential movement for tigers – and detailed the extensive current and future anthropogenic pressures on these important areas. Additionally, we found that while outputs from several popular techniques for simulating wildlife movement can predict in situ tiger occurrences, a circuit theory-based method, Circuitscape, performed best overall in this landscape and was the most robust to both inputs and validation data used for the analysis.
In Chapter 3, we analyzed a collection of jaguar telemetry data to understand how the environmental responses of jaguar movements vary depending on the behavioral state of the animal. We found that jaguars in a higher (i.e., exploratory) movement state were more likely to move through anthropogenic areas, low tree cover, and areas farther from high tree cover. As similar, less risk-averse behavior has been reported in other carnivores during larger scale movements such as dispersal, these exploratory movement patterns may be a proxy for dispersal movement tendencies and thus more applicable for connectivity planning for jaguars, particularly in mixed-use landscapes. Collectively, this research provides insight into the movement ecology of two threatened large carnivore species as well as multiple open-source methodologies for modeling movement that can be applied to other research questions and conservation objectives worldwide.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/fbj3-yb85 |
| Date | January 2024 |
| Creators | Schoen, Jay Michael |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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