Inter-population variability in movement parameters: practical implications for population density estimation

Motion-sensitive cameras are popular as non-invasive monitoring tools, and several methods have been developed to estimate population densities from camera data. These methods frequently rely on auxiliary movement data including the distance traveled by an individual in a day and the proportion of the day that an animal spends moving when individual recognition is not possible. The estimation of these movement parameters is time-consuming, which could limit the applicability of cameras to estimate population density. To investigate the relevance of measuring movement parameters for the target population, we monitored 54 wildlife populations of red deer (Cervus elaphus), fallow deer (Dama dama), roe deer (Capreolus capreolus), wild boar (Sus scrofa), and red fox (Vulpes vulpes) in different seasons through Europe with cameras. We estimated 91-day ranges and activity levels. We fitted mixed models for day range and activity level as response variables to assess if the inter-population variability in movement was explained by a set of a priori relevant geographical, environmental, biological, and management predictors. We then explored the bias in density estimates obtained in 25 independent populations when using predicted movement data. There was high intra-species variation in day range and activity level among species and populations. Only species explained a small proportion of this variability; other predictor variables did not. We observed bias in densities when predicting the day range and activity for independent populations. Considering the intra-species variability in movement parameters and the consequent unacceptable bias in density estimates, we recommend that monitoring and conservation programs estimate movement parameters for the target population and survey populations from camera data for more accurate density estimates. While this increases the handling time needed to estimate densities, it is worth the cost because of the reliability of camera-based methodologies to estimate needed movement parameters.