{'fraction_non_alphanumeric': 0.012440191387559809, 'fraction_numerical': 0.0, 'mean_word_length': 6.02013422818792, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Accounting for false positives improves estimates of occupancy from key informant interviews

Much research in conservation biogeography is fundamentally dependent on obtaining reliable data on species distributions across space and time. Such data are now increasingly being generated using various types of public surveys. These data are often integrated with occupancy models to evaluate distributional patterns, range dynamics and conservation status of multiple species at broad spatio‐temporal scales. Occupancy models have traditionally corrected for imperfect detection due to false negatives while implicitly assuming that false positives do not occur. However, public survey data are also prone to false‐positive errors, which when unaccounted for can cause bias in occupancy estimates. We test whether false positives in a dataset collected from public surveys lead to overestimation of species site occupancy and whether estimators that simultaneously account for false‐positive and false‐negative errors improve occupancy estimates.