@article{kirol_beck_huzurbazar_holloran_miller_2015, title={Identifying Greater Sage-Grouse source and sink habitats for conservation planning in an energy development landscape}, volume={25}, ISSN={["1939-5582"]}, DOI={10.1890/13-1152.1}, abstractNote={Conserving a declining species that is facing many threats, including overlap of its habitats with energy extraction activities, depends upon identifying and prioritizing the value of the habitats that remain. In addition, habitat quality is often compromised when source habitats are lost or fragmented due to anthropogenic development. Our objective was to build an ecological model to classify and map habitat quality in terms of source or sink dynamics for Greater Sage‐Grouse (Centrocercus urophasianus) in the Atlantic Rim Project Area (ARPA), a developing coalbed natural gas field in south‐central Wyoming, USA. We used occurrence and survival modeling to evaluate relationships between environmental and anthropogenic variables at multiple spatial scales and for all female summer life stages, including nesting, brood‐rearing, and non‐brooding females. For each life stage, we created resource selection functions (RSFs). We weighted the RSFs and combined them to form a female summer occurrence map. We modeled survival also as a function of spatial variables for nest, brood, and adult female summer survival. Our survival models were mapped as survival probability functions individually and then combined with fixed vital rates in a fitness metric model that, when mapped, predicted habitat productivity (productivity map). Our results demonstrate a suite of environmental and anthropogenic variables at multiple scales that were predictive of occurrence and survival. We created a source–sink map by overlaying our female summer occurrence map and productivity map to predict habitats contributing to population surpluses (source habitats) or deficits (sink habitat) and low‐occurrence habitats on the landscape. The source–sink map predicted that of the Sage‐Grouse habitat within the ARPA, 30% was primary source, 29% was secondary source, 4% was primary sink, 6% was secondary sink, and 31% was low occurrence. Our results provide evidence that energy development and avoidance of energy infrastructure were probably reducing the amount of source habitat within the ARPA landscape. Our source–sink map provides managers with a means of prioritizing habitats for conservation planning based on source and sink dynamics. The spatial identification of high value (i.e., primary source) as well as suboptimal (i.e., primary sink) habitats allows for informed energy development to minimize effects on local wildlife populations.}, number={4}, journal={ECOLOGICAL APPLICATIONS}, author={Kirol, Christopher P. and Beck, Jeffrey L. and Huzurbazar, Snehalata V. and Holloran, Matthew J. and Miller, Scott N.}, year={2015}, month={Jun}, pages={968–990} }
 @article{jhwueng_huzurbazar_o'meara_liu_2014, title={Investigating the performance of AIC in selecting phylogenetic models}, volume={13}, number={4}, journal={Statistical Applications in Genetics and Molecular Biology}, author={Jhwueng, D. C. and Huzurbazar, S. and O'Meara, B. C. and Liu, L.}, year={2014}, pages={459–475} }
 @article{gasch_huzurbazar_stahl_2014, title={Measuring soil disturbance effects and assessing soil restoration success by examining distributions of soil properties}, volume={76}, ISSN={["1873-0272"]}, DOI={10.1016/j.apsoil.2013.12.012}, abstractNote={Successful restoration of an ecosystem following disturbance is typically assessed according to similarity between the restored site and a relatively undisturbed reference area. While most comparisons use the average or mean parameter to represent measured properties, other aspects of the distribution, including the variance of the properties may assist in a more robust assessment of site recovery. Our purpose was to compare soil properties in different ages of reclaimed soils with those in reference areas by incorporating the potentially different distributions according to areas. On two sampling dates, in consecutive years, we examined soil properties on a chronosequence of reclaimed natural gas pipelines spanning recovery ages of <1–54 years, obtaining data on soil moisture, organic carbon, nitrogen, electrical conductivity, pH, and microbial abundance. To make the comparisons, we analyzed our data with a Bayesian hierarchical linear mixed model and obtained posterior predictive distributions for the soil properties. This allowed us to probabilistically quantify the extent to which a soil property from a reclaimed treatment was similar to that from an undisturbed reference. We found that the posterior predictive variance of most soil properties was particularly sensitive to disturbance and reclamation, especially, within the first few years of recovery. Response of this variance to disturbance, reclamation, and recovery was not necessarily accompanied by a shift in the posterior predictive mean value of the property. Patterns for all soil properties changed over time, with posterior predictive distributions of soil properties generally becoming more similar to those of the undisturbed reference sites as recovery time increased. We suspect these trends in altered variability coincide with the degree of spatial heterogeneity in soil properties that results following disturbance and reclamation, which is also coupled to patterns of vegetation recovery.}, journal={APPLIED SOIL ECOLOGY}, author={Gasch, Caley and Huzurbazar, Snehalata and Stahl, Peter}, year={2014}, month={Apr}, pages={102–111} }
 @article{huzurbazar_wick_gasch_stahl_2013, title={Bayesian posterior predictive distributions for assessing soil aggregation in undisturbed semiarid grasslands}, volume={77}, number={4}, journal={Soil Science Society of America Journal}, author={Huzurbazar, S. V. and Wick, A. F. and Gasch, C. K. and Stahl, P. D.}, year={2013}, pages={1380–1390} }
 @article{huzurbazar_singh_schlueter_2013, title={Statistical issues associated with modeling of synonymous mutation data}, volume={12}, number={3}, journal={Statistical Applications in Genetics and Molecular Biology}, author={Huzurbazar, S. and Singh, S. and Schlueter, J. A.}, year={2013}, pages={361–374} }