Summary1. Social network analyses tend to focus on human interactions. However, there is a burgeoning interest in applying graph theory to ecological data from animal populations. Here we show how radio-tracking and capture-mark-recapture data collated from wild rodent populations can be used to generate contact networks. 2. Both radio-tracking and capture-mark-recapture were undertaken simultaneously. Contact networks were derived and the following statistics estimated: mean-contact rate, edge distribution, connectance and centrality. 3. Capture-mark-recapture networks produced more informative and complete networks when the rodent density was high and radio-tracking produced more informative networks when the density was low. Different data collection methods provide more data when certain ecological characteristics of the population prevail. 4. Both sets of data produced networks with comparable edge (contact) distributions that were best described by a negative binomial distribution. Connectance and closeness were statistically different between the two data sets. Only betweenness was comparable. The differences between the networks have important consequences for the transmission of infectious diseases. Care should be taken when extrapolating social networks to transmission networks for inferring disease dynamics.

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Density estimates for small‐mammal populations from capture‐mark‐recapture (CMR) data have played an important role in many studies of theoretical and applied ecology. Defining effective trapping area (ETA) is one of the main issues affecting accuracy of density estimates. Our objective was to assess sensitivity of CMR density estimates to correctors based on movement parameters calculated from trapping and radiotelemetry data. From May to November 2005, we conducted monthly CMR trapping in a beech (Fagus sylvaticus) forest of the province of Trento, northern Italy. In conjunction with CMR, we radio‐marked 32 yellow‐necked mice (Apodemus flavicollis) captured from July to October and located them daily using radiotelemetry. We estimated population size (N) by model averaging with Program MARK. We calculated ETA using several definitions of the boundary strip, including full and half mean maximum distance moved (MMDM) from capture‐recapture and telemetry data and mean radius of mean monthly home ranges. The boundary strip (W) increased with the amount of behavioral information embodied in the estimates. The largest W and lowest density values were based on radius of mean home ranges followed by MMDM calculated from telemetry data. The ETA based on movement distances increased more than proportionally when N decreased, suggesting that low population density combined with scarce resources results in rodents moving more in search of food, thus leading to overestimated ETA and underestimated density values. Although robust behavioral information would certainly improve density estimates, we suggest caution in relating ranging movements to capture probability and hence in using correctors based on movement distances to infer density values.