Browsing by Author "Li, Yan"
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- Evaluating spatial and temporal variability in growth and mortality for recreational fisheries with limited catch dataLi, Yan; Wagner, Tyler K.; Jiao, Yan; Lorantas, Robert; Murphy, Cheryl A. (2018-09)Understanding the spatial and temporal variability in life-history traits among populations is essential for the management of recreational fisheries. However, valuable freshwater recreational fish species often suffer from a lack of catch information. In this study, we demonstrated the use of an approach to estimate the spatial and temporal variability in growth and mortality in the absence of catch data and apply the method to riverine smallmouth bass (Micropterus dolomieu) populations in Pennsylvania, USA. Our approach included a growth analysis and a length-based analysis that estimates mortality. Using a hierarchical Bayesian approach, we examined spatial variability in growth and mortality by assuming parameters vary spatially but remain constant over time and temporal variability by assuming parameters vary spatially and temporally. The estimated growth and mortality of smallmouth bass showed substantial variability over time and across rivers. We explored the relationships of the estimated growth and mortality with spring water temperature and spring flow. Growth rate was likely to be positively correlated with these two factors, while young mortality was likely to be positively correlated with spring flow. The spatially and temporally varying growth and mortality suggest that smallmouth bass populations across rivers may respond differently to management plans and disturbance such as environmental contamination and land-use change. The analytical approach can be extended to other freshwater recreational species that also lack of catch data. The approach could also be useful in developing population assessments with erroneous catch data or be used as a model sensitivity scenario to verify traditional models even when catch data are available.
- Investigating ecosystem-level effects of gillnet bycatch in Lake Erie: implications for commercial fisheries managementLi, Yan (Virginia Tech, 2010-07-22)Lake Erie supports one of the world's largest freshwater commercial fisheries. Bycatch has become a concern in current fisheries management. This study focused on four species in Lake Erie that include two major commercial and recreational species, walleye (Sander vitreus) and yellow perch (Perca flavescens); an invasive species, white perch (Morone americana); and an endangered species, lake sturgeon (Acipenser fulvescens). The analyses were based on two datasets, the Partnership Index Fishing Survey (PIS) data and the commercial gillnet logbook data. The bycatch of walleye, yellow perch and white perch was predicted by a delta model developed on the PIS data. Discards were estimated as the difference between predicted bycatch and landed bycatch. Results highlighted bycatch and discard hotspots for these three species that have great management implications. Three classification tree models, a conditional inference tree and two exhaustive search-based trees, were constructed using the PIS data to estimate the probability of obtaining lake sturgeon bycach under specific environmental and gillnet fishing conditions. Lake sturgeon bycatch was most likely to be observed in the west basin of Lake Erie. The AdaBoost algorithm was applied in conjunction with the generalized linear/additive models to analyze catch rates of walleye, yellow perch and white perch. Three- and five-fold cross-validations were conducted to evaluate the performance of each candidate model. Results indicated that the Delta-AdaBoost model yielded the smallest training error and test error on average. I recommend the Delta-AdaBoost model for catch and bycatch analyses when data contain a high percentage of zeros.
- Spatial dynamics modeling for data-poor species using examples of longline seabird bycatch and endangered white abaloneLi, Yan (Virginia Tech, 2014-05-20)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.