Habitat quality influences individual survival at widely varying spatial and temporal scales. Understanding interactions between habitat and survival among individuals in declining populations that occupy highly modified landscapes can inform conservation strategies aimed at improving survival and population growth. We used radiotelemetry to monitor space use and daily survival of wintering Northern Bobwhites (Colinus virginianus) at the northern end of their range to test for fine spatial- and temporal-scale relationships between individual survival and habitat composition around radio-locations in agricultural landscapes in Ohio, USA. Habitat composition within daily and seasonal movement ranges of individuals (n = 189) during periods without snow cover did not explain variation in daily survival rates. However, mortality increased substantially in the presence of snow cover, and availability of woody cover and row crops within 95 m of an individual radio-location were positively associated with daily survival during those periods. A similar relationship between row crop availability and survival was supported at a larger scale that reflected composition of seasonal ranges (300-m buffer) but was less influential than fine-scale influences of woody cover and row crops. Our results suggest that previously documented selection for woody cover in our agricultural study areas was an adaptive behavior to improve individual survival during periods of snow cover. Positive associations between survival and row crop cover at daily and seasonal range scales suggest that agricultural landscapes confer improved survival probabilities when underlying constraints on occupancy related to woody cover are met. Collectively, our results suggest that targeted conservation practices focused on provision of suitable woody cover in agricultural landscapes in the northern end of the Northern Bobwhite's range has potential to improve winter survival and perhaps abate long-term population declines in the region.
Vol. 117 • No. 1
Vol. 117 • No. 1
generalized linear mixed models