Spatial dynamics modeling for data-poor species using examples of longline seabird bycatch and endangered white abalone

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2014-05-20
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Virginia Tech
Abstract

Spatial analysis of species for which there is limited quantity of data, termed as the data-poor species, has been challenging due to limited information, especially lack of spatially explicit information. However, these species are frequently of high ecological, conservation and management interest. In this study, I used two empirical examples to demonstrate spatial analysis for two kinds of data-poor species. One example was seabird bycatch from the U.S. Atlantic pelagic longline fishery, which focused on rare events/species for which data are generally characterized by a high percentage of zero observations. The other example was endangered white abalone off the California coast, which focused on endangered species whose data are very limited. With the seabird bycatch example, I adopted a spatial filtering technique to incorporate spatial patterns and to improve model performance. The model modified with spatial filters showed superior performance over other candidate models. I also applied the geographically weighted approach to explore spatial nonstationarity in seabird bycatch, i.e., spatial variation in the parameters that describe relationships between biological processes and environmental factors. Estimates of parameters exhibited high spatial variation. With the white abalone example, I demonstrated the spatially explicit hierarchical demographic model and conducted a risk assessment to evaluate the efficacy of hypothetical restoration strategies. The model allowed for the Allee effect (i.e., density-dependent fertilization success) by using spatial explicit density estimates. Restoration efforts directed at larger-size individuals may be more effective in increasing population density than efforts focusing on juveniles. I also explored the spatial nonstationarity in white abalone catch data. I estimated the spatially explicit decline rate and linked the decline rate to environmental factors including water depth, distance to California coast, distance to land, sea surface temperature and chlorophyll concentration. The decline rate showed spatial variation. I did not detect any significant associations between decline rate and these five environmental factors. Through such a study, I am hoping to provide insights on applying or adapting existing methods to model spatial dynamics of data-poor species, and on utilizing information from such analyses to aid in their conservation and management.

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spatial nonstationarity, spatial filter, risk assessment, data-poor species, zero-inflated data, demographic model, delta model
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