Abstract. Animal resource requirements differ among life-history stages, and thus, habitat is most appropriately thought of as specific to a particular life stage. Accordingly, different habitats may vary in their significance as functions of (1) the sensitivity of population growth to the life stage for which the habitat is most important, (2) spatial association of each habitat to other habitats, and (3) the abundance of the habitat in question. We used an analogy to a life-table response experiment to develop spatial models linking key habitats to rates of population increase in Greater Sage-grouse. We parameterized models linking demographic rates to vegetation and physical attributes of habitats, including spatial association of some habitats to others, using a decade-long study of Greater Sage-grouse in central Nevada. We modeled the contribution of each pixel in the landscape to regional k (finite rate of population increase) using functional relationships between demographic rates and the attributes of that pixel, and the sensitivity of k to each demographic rate. We incorporated the following demographic rates into our model: female nesting success, survival of chicks from hatching to 45 d, and adult female annual survival. We also incorporated the probability a site was used for nesting. Chick survival (62%) and nest site selection (21%) explained most of the variance in lambda. We found that only~8% of all habitat for Greater Sage-grouse contributed to k > 1. Habitat supporting population growth occurred in mid-high elevation areas with moderate slopes, and a high percent cover of sagebrush, and in nesting areas close to late-brood habitat. Our models indicate that a relatively small proportion of habitat available to Greater Sage-grouse in central Nevada is responsible for maintenance of the population in our study system. We suggest that the general approach we describe here can be used to improve understanding of habitats most likely to regulate populations in other systems, providing an important tool in ecology and conservation.

Standardised follow-up data can be obtained on the majority of children undergoing benchmark operations. Ongoing efforts to support automated electronic systems and integration with registry data may reduce resource needs, facilitate expansion across centres, and support multi-centre efforts to understand and improve long-term outcomes in this population.