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Passive integrated transponder (PIT) tags allow a range of individual‐level data to be collected passively and have become a commonly used technology in many avian studies. Although the potential adverse effects of PIT tags have been evaluated in several species, explicit investigations of their impacts on small (, We investigated lethal (mortality) and nonlethal (body condition) effects of different two PIT tagging methods (implants or leg band embedded) compared with birds that were color banded but not fitted with PIT tags. In a sample of over 200 birds followed for more than two years, we found no evidence of differences in survival or body condition across any of these three treatments.

Designing an effective conservation strategy requires understanding where rare species are located. Because rare species can be difficult to find, ecologists often identify other species called conservation surrogates that can help inform the distribution of rare species. Species distribution models typically rely on environmental data when predicting the occurrence of species, neglecting the effect of species' co‐occurrences and biotic interactions. Here, we present a new approach that uses Bayesian networks to improve predictions by modeling environmental co‐responses among species. For species from a European peat bog community, our approach consistently performs better than single‐species models and better than conventional multi‐species approaches that include the presence of nontarget species as additional independent variables in regression models. Our approach performs particularly well with rare species and when calibration data are limited. Furthermore, we identify a group of “predictor species” that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices., It is difficult to protect rare species from extinction because of their sparse distributions and specialized habitat preferences. Here, we use Bayesian networks to accurately predict the occurrence of rare species, providing a solution to an ever‐important conservation problem.

Designing an effective conservation strategy requires understanding where rare species are located. Although species distribution models are primarily used to identify patterns at large spatial scales, their general methodology is relevant for predicting the occurrence of individual species at specific locations. Here we present a new approach that uses Bayesian networks to improve predictions by modelling environmental co-responses among species. For species from a European peat bog community, our approach consistently performs better than single-species models, and better than conventional multi-species models for rare species when calibration data are limited. Furthermore, we identify a group of “predictor species” that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices.