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3 results for “age-specific mortality”
Data from: Age-specific offspring mortality economically tracks food abundance in a piscivorous seabird
Earlier offspring mortality prior to independence saves resources for kin, which should be more beneficial when food is short. Using 24 years of data on age-specific common tern (Sterna hirundo) chick mortality, best described by the Gompertz function, and estimates of energy consumption per age of mortality, we investigated how energy wasted on non-fledged chicks depends on brood size, hatching order and annual abundance of herring (Clupea harengus), the main food source. We found mortality directly after hatching (Gompertz baseline mortality) to be high and to increase with decreasing herring abundance. Mortality declined with age, at a rate relatively insensitive to herring abundance. The sensitivity of baseline mortality to herring abundance reduced energy wasted on non-fledged chicks when herring was short. Among chicks that did not fledge, last-hatched chicks were less costly than earlier hatched chicks, due to their earlier mortality. However, per hatchling produced, the least energy was wasted on chicks without siblings, due to their baseline mortality being most sensitive to herring abundance. We suggest that earlier mortality of offspring when food is short facilitates economic adjustment of post-hatching parental investment to food abundance, but that such economic brood reduction may be constrained by sibling competition.
Data from: Age-specific offspring mortality economically tracks food abundance in a piscivorous seabird
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Age-Specific Mortality Climate Impact Distributions
<p>Percentiles of age-specific mortality impacts due to climate change, organized by level of aggregation, climate scenario, and time period.</p> <p>Mortality impacts are estimated by applying future climate data to dose-response functions of mortality rates for specific age bands.</p> <p>The uncertainty represented by these percentiles comes from a combination of statistical uncertainty in the dose-response functions, climate uncertainty across GCMs, and within-month weather realization, as sampled by Monte Carlo runs.</p> <p>The archive contains four folders: county_20a with county-level impacts, state_20yr with state-aggregated impacts, nca_20yr with NCA-region aggregated impacts, and national_20y with nationally aggregated impacts. Aggregation is weighted by population within each region</p> <p>The files are labeled as follow:</p> <p>health-mortage-<BAND>-<RCP>-<YEAR><SUFFIX>.</p> <p>The age BAND is one of 0-0 (newborns), ages 1 - 44, 45 - 64, and 65-inf (older than 64). The RCP may be RCP 2.6, RCP 4.5, RCP 6.0, or RCP 8.5; the years are "2020", for average impacts during 2020 - 2039, "2040" for impacts 2040 - 2059, and "2080" for impacts 2080 - 2099. Files with the suffix 'b.csv' contain percent changes; those with the suffix '-absolute.csv' contain level changes in MT.</p> <p>The columns of these files specify the region (using the FIPS code for counties), and the quantiles from 1% ("q0.01") to 99% ("q0.99").</p> <p><em>This data is provided for non-commercial research and educational purposes.</em></p>
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