The distribution of sagebrush (Artemisia spp.) within the Sage-Grouse Conservation Area (SGCA, the historical distribution of sage-grouse buffered by 50 km) stretches from British Columbia and Saskatchewan in the north, to northern Arizona and New Mexico in the south, and from the eastern slopes of the Sierra Nevada and Cascade mountains to western South Dakota. The dominant sagebrush (sub)species as well as the composition and proportion of shrubs, grasses, and forbs varies across different ecological sites as a function of precipitation, temperature, soils, topographic position, elevation, and disturbance history. Most important to Greater Sage-Grouse (Centrocercus urophasianus) are three subspecies of big sagebrush (Artemisia tridentata)(basin big sagebrush [A. t. ssp. tridentata], Wyoming big sagebrush [A. t. ssp. wyomingensis], and mountain big sagebrush [A. t. ssp. vaseyana]); two low or dwarf forms (little sagebrush [A. arbuscula] and black sagebrush [A. nova]); and silver sagebrush (A. cana), which occurs primarily in the northeast portion of the sage-grouse range. Invasive plant species, wildfires, and weather and climate change are major influences on sagebrush habitats and present significant challenges to their long-term conservation. Each factor is spatially pervasive across the Greater Sage-Grouse Conservation Area 145

We developed a simulation method, known as life‐stage simulation analysis (LSA) to measure potential effects of uncertainty and variation in vital rates on population growth (λ) for purposes of species conservation planning. Under LSA, we specify plausible or hypothesized levels of uncertainty, variation, and covariation in vital rates for a given population. We use these data under resampling simulations to establish random combinations of vital rates for a large number of matrix replicates and finally summarize results from the matrix replicates to estimate potential effects of each vital rate on λ in a probability‐based context. Estimates of potential effects are based on a variety of summary statistics, such as frequency of replicates having the same vital rate of highest elasticity, difference in elasticity values calculated under simulated conditions vs. elasticities calculated using mean invariant vital rates, percentage of replicates having positive population growth, and variation in λ explained by variation in each vital rate. To illustrate, we applied LSA to vital rates for two vertebrates: desert tortoise (Gopherus agassizii) and Greater Prairie Chicken (Tympanuchus cupido). Results for the prairie chicken indicated that a single vital rate consistently had greatest effect on population growth. Results for desert tortoise, however, suggested that a variety of life stages could have strong effects on population growth. Additional simulations for the Greater Prairie Chicken under a hypothetical conservation plan also demonstrated that a variety of vital rates could be manipulated to achieve desired population growth. To improve the reliability of inference, we recommend that potential effects of vital rates on λ be evaluated using a probability‐based approach like LSA. LSA is an important complement to other methods that evaluate vital‐rate effects on λ, including classical elasticity analysis, retrospective methods of variance decomposition, and simulation of the effects of environmental stochasticity.

Matrix population models have entered the mainstream of conservation biology, with analysis of proportional sensitivities (elasticity analysis) of demographic rates becoming important components of conservation decision making. We identify areas where management applications using elasticity analysis potentially conflict with the mathematical basis of the technique, and we use a hypothetical example and three real data sets (Prairie Chicken [  Tympanuchus cupido], desert tortoise [Gopherus agassizii], and killer whale [Orcinus orca]) to evaluate the extent to which conservation recommendations based on elasticities might be misleading. First, changes in one demographic rate can change the qualitative ranking of the elasticity values calculated from a population matrix, a result that dampens enthusiasm for ranking conservation actions based solely on which rates have the highest elasticity values. Second, although elasticities often provide accurate predictions of future changes in population growth rate under management perturbations that are large or that affect more than one rate concurrently, concordance frequently fails when different rates vary by different amounts. In particular, when vital rates change to their high or low values observed in nature, predictions of future growth rate based on elasticities of a mean matrix can be misleading, even predicting population increase when the population growth rate actually declines following a perturbation. Elasticity measures will continue to be useful tools for applied ecologists, but they should be interpreted with considerable care. We suggest that studies using analytical elasticity analysis explicitly consider the range of variation possible for different rates and that simulation methods are a useful tool to this end.

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