Spatial capture-recapture (SCR) models are powerful analytical tools that have become the standard for estimating abundance and density of wild animal populations. When sampling populations to implement SCR, the number of unique individuals detected, total recaptures, and unique spatial relocations can be highly variable. These sample sizes influence the precision and accuracy of model parameter estimates. Testing the performance of SCR models with sparse empirical data sets typical of low-density, wide-ranging species can inform the threshold at which a more integrated modeling approach with additional data sources or additional years of monitoring may be required to achieve reliable, precise parameter estimates. Using a multi-site, multi-year Utah black bear (Ursus americanus) capture-recapture data set, we evaluated factors influencing the uncertainty of SCR structural parameter estimates, specifically density, detection, and the spatial scale parameter, sigma. We also provided some of the first SCR density estimates for Utah black bear populations, which ranged from 3.85 to 74.33 bears/100 km 2 . Increasing total detections decreased the uncertainty of density estimates, whereas an increasing number of total recaptures and individuals with recaptures decreased the uncertainty of detection and sigma estimates, respectively. In most cases, multiple years of data were required for precise density estimates (<0.2 coefficient of variation [CV]). Across study areas there was an average decline in CV of 0.07 with the addition of another year of data. One sampled population with very high estimated bear density had an atypically low number of spatial recaptures relative to total recaptures, apparently inflating density estimates. A complementary simulation study used to assess estimate bias suggested that when <30% of recaptured individuals were spatially recaptured, density estimates were unreliable and ranged widely, in some cases to >3 times the simulated density. Additional research could evaluate these requirements for other density scenarios. Large numbers of individuals detected, numbers of spatial

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Dytiscus latissimus (Coleoptera Dytiscidae) is an endangered diving beetle throughout its range. It is one of the two species of Dytiscidae listed in Annex II of the Habitats Directive, IUCN red list and in many national level legislations and therefore strictly protected. The conservation of endangered species first of all requires an assessment of their population size. Until now, a method has not been developed for estimating the size of D. latissimus populations. The article summarizes the results of two studies carried out independently in Germany and Latvia. Both studies were carried out in one water body used recapture method but with a different spatial placement of traps, which, according to our data, is an important factor in population estimation. We evaluated Jolly-Seber and Schnabel approaches of estimating aquatic beetle's populations and found that confidence intervals obtained by different methods in our research do not differ significantly, but combination of both models provide the most accurate estimates of population dynamics. As part of the study, we concluded that the populations of Dytiscus latissimus are relatively closed, so we accept that the Schnabel estimate shows more accurate data. By fixing the places of capture of each individual, it was found that females live mainly locally, and males actively move within the water body. This aspect indicates the advantage of the spatial placement of traps compared to the use of transects. The results of our study show a significantly higher number of both captured and recaptured males Such a sex ratio may indicate both a greater activity of males and differences in the sex ratio in the population. The study confirmed that environmental changes, such as the water level in a water body, can also significantly affect the result of a population assessment. In the frame of D. latissimus monitoring, to obtain an objective estimation of the species population size we recommend using four traps for each 100 m of water body shoreline with 4–8 censuses, dependently on the recapture rate.