Mapping wintering waterfowl distributions using weather surveillance radar.

The current network of weather surveillance radars within the United States readily detects flying birds and has proven to be a useful remote-sensing tool for ornithological study. Radar reflectivity measures serve as an index to bird density and have been used to quantitatively map landbird distributions during migratory stopover by sampling birds aloft at the onset of nocturnal migratory flights. Our objective was to further develop and validate a similar approach for mapping wintering waterfowl distributions using weather surveillance radar observations at the onset of evening flights. We evaluated data from the Sacramento, CA radar (KDAX) during winters 1998-1999 and 1999-2000. We determined an optimal sampling time by evaluating the accuracy and precision of radar observations at different times during the onset of evening flight relative to observed diurnal distributions of radio-marked birds on the ground. The mean time of evening flight initiation occurred 23 min after sunset with the strongest correlations between reflectivity and waterfowl density on the ground occurring almost immediately after flight initiation. Radar measures became more spatially homogeneous as evening flight progressed because birds dispersed from their departure locations. Radars effectively detected birds to a mean maximum range of 83 km during the first 20 min of evening flight. Using a sun elevation angle of -5° (28 min after sunset) as our optimal sampling time, we validated our approach using KDAX data and additional data from the Beale Air Force Base, CA (KBBX) radar during winter 1998-1999. Bias-adjusted radar reflectivity of waterfowl aloft was positively related to the observed diurnal density of radio-marked waterfowl locations on the ground. Thus, weather radars provide accurate measures of relative wintering waterfowl density that can be used to comprehensively map their distributions over large spatial extents.

Recurrent diabetic ketoacidosis in inner-city minority patients: behavioral, socioeconomic, and psychosocial factors.

OBJECTIVE: To conduct a bedside study to determine the factors driving insulin noncompliance in inner-city patients with recurrent diabetic ketoacidosis (DKA). RESEARCH DESIGN AND METHODS: We analyzed socioeconomic and psychological factors in 164 adult patients with DKA who were admitted to Grady Hospital between July 2007 and August 2010, including demographics, diabetes treatment, education, and mental illness. The Patient Health Questionnaire-9 and the Short Form-36 surveys were used to screen for depression and assess quality of life. RESULTS: The average number of admissions was 4.5 ± 7 per patient. A total of 73 patients presented with first-time DKA, and 91 presented with recurrent DKA; 96% of patients were African American. Insulin discontinuation was the leading precipitating cause in 68% of patients; other causes were new-onset diabetes (10%), infection (15%), medical illness (4%), and undetermined causes (3%). Among those who stopped insulin, 32% gave no reasons for stopping, 27% reported lack of money to buy insulin, 19% felt sick, 15% were away from their supply, and 5% were stretching insulin. Compared with first-time DKA, those with recurrent episodes had longer duration of diabetes (P < 0.001), were a younger age at the onset of diabetes (P = 0.04), and had higher rates of depression (P = 0.04), alcohol (P = 0.047) and drug (P < 0.001) abuse, and homelessness (P = 0.005). There were no differences in quality-of-life scores, major psychiatric illnesses, or employment between groups. CONCLUSIONS: Poor adherence to insulin therapy is the leading cause of recurrent DKA in inner-city patients. Several behavioral, socioeconomic, psychosocial, and educational factors contribute to poor compliance. The recognition of such factors and the institution of culturally appropriate interventions and education programs might reduce DKA recurrence in minority populations.

Understanding interaction effects of climate change and fire management on bird distributions through combined process and habitat models.

Avian conservation efforts must account for changes in vegetation composition and structure associated with climate change. We modeled vegetation change and the probability of occurrence of birds to project changes in winter bird distributions associated with climate change and fire management in the northern Chihuahuan Desert (southwestern U.S.A.). We simulated vegetation change in a process-based model (Landscape and Fire Simulator) in which anticipated climate change was associated with doubling of current atmospheric carbon dioxide over the next 50 years. We estimated the relative probability of bird occurrence on the basis of statistical models derived from field observations of birds and data on vegetation type, topography, and roads. We selected 3 focal species, Scaled Quail (Callipepla squamata), Loggerhead Shrike (Lanius ludovicianus), and Rock Wren (Salpinctes obsoletus), that had a range of probabilities of occurrence for our study area. Our simulations projected increases in relative probability of bird occurrence in shrubland and decreases in grassland and Yucca spp. and ocotillo (Fouquieria splendens) vegetation. Generally, the relative probability of occurrence of all 3 species was highest in shrubland because leaf-area index values were lower in shrubland. This high probability of occurrence likely is related to the species' use of open vegetation for foraging. Fire suppression had little effect on projected vegetation composition because as climate changed there was less fuel and burned area. Our results show that if future water limits on plant type are considered, models that incorporate spatial data may suggest how and where different species of birds may respond to vegetation changes.