Fundamentals of wildlife dosimetry and lessons learned from a decade of measuring external dose rates in the field.

Methods for determining the radiation dose received by exposed biota require major improvements to reduce uncertainties and increase precision. We share our experiences in attempting to quantify external dose rates to free-ranging wildlife using GPS-coupled dosimetry methods. The manuscript is a primer on fundamental concepts in wildlife dosimetry in which the complexities of quantifying dose rates are highlighted, and lessons learned are presented based on research with wild boar and snakes at Fukushima, wolves at Chornobyl, and reindeer in Norway. GPS-coupled dosimeters produced empirical data to which numerical simulations of external dose using computer software were compared. Our data did not support a standing paradigm in risk analyses: Using averaged soil contaminant levels to model external dose rates conservatively overestimate the dose to individuals within a population. Following this paradigm will likely lead to misguided recommendations for risk management. The GPS-dosimetry data also demonstrated the critical importance of how modeled external dose rates are impacted by the scale at which contaminants are mapped. When contaminant mapping scales are coarse even detailed knowledge about each animal's home range was inadequate to accurately predict external dose rates. Importantly, modeled external dose rates based on a single measurement at a trap site did not correlate to actual dose rates measured on free ranging animals. These findings provide empirical data to support published concerns about inadequate dosimetry in much of the published Chernobyl and Fukushima dose-effects research. Our data indicate that a huge portion of that literature should be challenged, and that improper dosimetry remains a significant source of controversy in radiation dose-effect research.

Shining a light on elusive lynx: Density estimation of three Eurasian lynx populations in Ukraine and Belarus.

The Eurasian lynx is a large carnivore widely distributed across Eurasia. However, our understanding of population status is heterogeneous across their range, with some populations isolated that are at risk of reduced genetic variation and a complete lack of information about others. In many European countries, Eurasian lynx are monitored through demographic studies crucial for their conservation and management. Even so, there are only rough and fragmented population assessments from Ukraine and Belarus, despite strict protection in both countries and their importance for lynx connectivity across Europe. We monitored lynx from October 2020 to March 2021 and used camera trapping in combination with spatial capture-recapture (SCR) methods in a Bayesian framework to provide the first SCR density estimation of three lynx populations across Ukraine and Belarus, including the Ukrainian Chornobyl Exclusion Zone, southern Belarus and the Ukrainian Carpathians. Our density estimates varied within our study areas ranging from 0.45 to 1.54 individuals/100 km2. This work provides a substantial scientific component to the overall understanding of lynx conservation for a region where only broad information is available and opens the doors for further large-scale monitoring and trend assessments. The crucial information we provide can greatly enhance the range-wide assessments of the status of this protected species. We also discuss the implications for Eurasian lynx conservation, despite the geopolitical realities impacting species monitoring in the region. Our work serves as a baseline, not only for future conservation interventions but also to evaluate the effects of disturbance and threats to these protected populations.

GPS-coupled contaminant monitors on free-ranging Chernobyl wolves challenge a fundamental assumption in exposure assessments.

Measurements of external contaminant exposures on individual wildlife are rare because of difficulties in using contaminant monitors on free-ranging animals. Most wildlife contaminant exposure data are therefore simulated with computer models. Rarely are empirical exposure data available to verify model simulations, or to test fundamental assumptions inherent in exposure assessments. We used GPS-coupled contaminant monitors to quantify external exposures to individual wolves (Canis lupus) living within the Belarus portion of Chernobyl's 30-km exclusion zone. The study provided data on animal location and contaminant exposure every 35 min for 6 months, resulting in ~6600 individual locations and 137Cs external exposure readings per wolf, representing the most robust external exposure data published to date on free ranging animals. The data provided information on variation in external exposure for each animal over time, as well as variation in external exposure among the eight wolves across the landscape of Chernobyl. The exposure data were then used to test a fundamental assumption in screening-level risk assessments, espoused in guidance documents of the U.S. Environmental Protection Agency and U.S. Department of Energy, - Mean contaminant concentrations conservatively estimate individual external exposures. We tested this assumption by comparing our empirical data to a series of simulations using the ERICA modeling tool. We found that modeled simulations of mean external exposure (10.5 mGy y-1), based on various measures of central tendency, under-predicted mean exposures measured on five of the eight wolves wearing GPS-contaminant monitors (i.e., 12.3, 26.3, 28.0, 28.8 and 35.7 mGy y-1). If under-prediction of exposure occurs for some animals, then arguably the use of averaged contaminant concentrations to predict external exposure is not as conservative as proposed by current risk assessment guidance. Thus, a risk assessor's interpretation of simulated exposures in a screening-level risk analysis might be misguided if contaminant concentrations are based on measures of central tendency. We offer three suggestions for risk assessors to consider in order to reduce the probability of underestimating exposure in a screening-level risk assessment.