Methodologies for abundance estimation of moose (Alces alces) and other rare species
Supplemental file(s) description: Survey transects for moose research -- 2016, Survey transects for moose research -- 2017 Moose (Alces alces) are a species of management concern in New York State. In some New England states, moose populations are known to be in decline due to mortality from parasit...
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| Other Authors: | , , |
| Format: | Thesis |
| Language: | English |
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2018
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| Online Access: | https://hdl.handle.net/1813/64919 http://dissertations.umi.com/cornell:10418 https://doi.org/10.7298/6adv-ae33 |
| Summary: | Supplemental file(s) description: Survey transects for moose research -- 2016, Survey transects for moose research -- 2017 Moose (Alces alces) are a species of management concern in New York State. In some New England states, moose populations are known to be in decline due to mortality from parasitic infection, thermal stress, nutritional deficiency, and moose-vehicle collisions. In contrast, the status of the New York moose population has not been described satisfactorily; abundance has increased since the species' recolonization in 1980, but indices of abundance such as moose-vehicle collisions and public sightings do not reflect the growth of other northeastern U.S. states. In 2015, The New York State Department of Environmental Conservation initiated the project described herein to examine aspects of this population of moose, most notably the size of the population. This thesis offers (a) an advancement of spatial capture-recapture (SCR) methodology to quantify the abundance of rare species through the integration of adaptive sampling principles, and (b) an alternative solution to SCR that estimates population size from scat collections made by detection dogs, without knowledge of individual identity. Rare species present challenges to data collection, particularly when the species is spatially clustered over large areas, such that the encounter frequency of the organism is low. Sampling where the organism is absent consumes resources, and offers relatively low-quality information which are often difficult to model using standard statistical methods. In adaptive sampling, a probabilistic sampling method is employed first, and additional effort is allocated in the vicinity of sites where some measured index variable - assumed to be proportional to local population size - exceeds an a priori threshold. We applied this principle to the spatial capture-recapture (SCR) analytical framework in a Bayesian hierarchical model incorporating capture-recapture (CR) and index information from unsampled sites to ... |
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