We provide an analysis of the galactic cosmic ray radiation environment of Earth's atmosphere using measurements from the Cosmic Ray Telescope for the Effects of Radiation (CRaTER) aboard the Lunar Reconnaissance Orbiter (LRO) together with the Badhwar‐O'Neil model and dose lookup tables generated by the Earth‐Moon‐Mars Radiation Environment Module (EMMREM). This study demonstrates an updated atmospheric radiation model that uses new dose tables to improve the accuracy of the modeled dose rates. Additionally, a method for computing geomagnetic cutoffs is incorporated into the model in order to account for location‐dependent effects of the magnetosphere. Newly available measurements of atmospheric dose rates from instruments aboard commercial aircraft and high‐altitude balloons enable us to evaluate the accuracy of the model in computing atmospheric dose rates. When compared to the available observations, the model seems to be reasonably accurate in modeling atmospheric radiation levels, overestimating airline dose rates by an average of 20%, which falls within the uncertainty limit recommended by the International Commission on Radiation Units and Measurements (ICRU). Additionally, measurements made aboard high‐altitude balloons during simultaneous launches from New Hampshire and California provide an additional comparison to the model. We also find that the newly incorporated geomagnetic cutoff method enables the model to represent radiation variability as a function of location with sufficient accuracy.

Assumptions about how conservation practices will affect ecological outcomes are critical for informing and learning from conservation actions. However, when assumptions do not reflect conditions to which they are applied, they can impede achievement of targeted outcomes and hinder capacity to contribute to conservation goals. We assert that identifying and examining technical assumptions, or those that relate to abiotic or biotic systems, in conservation practice retrospectively for broad conservation strategies is crucial for advancing learning in conservation. Unlike existing proactive assumption frameworks, retroactive examination, which is often realistic for broad scale conservation, allows for honest evaluation of the contributions of those strategies toward shared goals.We propose the state, identify, focus, and think (SIFT) framework, a four-step process, to guide examination of technical assumptions by defining how assumptions interact with biological circumstances to shape outcomes. We demonstrate use of the SIFT framework with a common technical assumption in US federal private lands conservation programs-that all acres are similarly valuable for achieving wildlife conservation benefits. With the SIFT framework, we show that the benefits of these programs are likely to be applicable to mobile, generalist species with small space requirements, while many species of conservation concern are less likely to benefit.

Informative species abundance estimates are critical for guiding decisions around the conservation and management of ecological systems. There exist many methods for estimating abundance of frequently encountered species and populations with uniquely identifiable individuals. However, for wildlife populations with unmarked individuals that occur at low densities, there exist a variety of behaviors and characteristics that make effectively surveying and sampling challenging or uninformative. Examples of challenging characteristics include the elusive behaviors of low-density species that occur in complex and rugged terrain. Such characteristics make detection difficult and surveys expensive, dangerous, and potentially biased. To address these challenges, we used a common, non-invasive field survey method combined with a probability-based study design and frequently utilized statistical model to estimate abundance of an unmarked mountain goat population in eastern Idaho. We developed a novel data analysis approach using an N-mixture model that, together with spatially balanced random sampling and a double-observer field data collection method, directly solves the problem of approximating statistical assumptions, including population closure. We demonstrate that a probability-based sampling design not only is feasible, but also is important for estimating population parameters for unmarked and low-density species. With this approach, we present a procedure that offers unbiased abundance estimates, empowering managers to track low-density species' population trends across time.

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