Spatial, temporal, and motivational changes due to the COVID-19 pandemic in a nature-based leisure activity - A global survey of birders.

Birdwatchers contribute an immense amount of data to citizen science databases. Thus, birding is important from the leisure perspective and from nature conservation. During the COVID-19 pandemic, we studied birdwatchers on a global scale in over 50 countries by applying the model of behavior change, which focuses on changes in opportunity (spatial, temporal), motivation, and capability (avoidance behavior). The sample consisted of 5051 participants (3437 men, 1575 women, mean age 49 years). Birders changed their spatial behavior to more local birding and to avoidance behavior by choosing different places and different clock times. Concerning motivation, being outdoors showed the highest increase and being with friends the strongest decrease. Higher specialized birders experienced a stronger shift toward regional birding. Birders that focused on new, local, or unrewarding places experienced an increase in motivation. Our study empirically supports the behavior change model and highlights the need to address the heterogeneity of the recreationists.

Assessing the effect of weather on human outdoor perception using Twitter.

Human comfort in outdoor spaces (HCOS) is linked to people's psychological responses to environmental variables. Previous studies have established comfort ranges for these variables through interviews and questionnaires, reaching only limited populations. However, larger amounts of data would not only generate more robust results in local studies, but it would also allow for the possibility of creating an approach that could be applied to a wider range of weather conditions and different climates. Therefore, this study describes a new methodology to assess people's perception of weather based on human responses to weather conditions extracted from tweets, with the purpose of establishing comfort ranges for environmental variables. Tweets containing weather-associated keywords were collected, stored, and then linked to real-time meteorological data acquired nearby the locations in which the tweets were posted. Afterwards, people's perception of weather was extracted from the tweets using a classifier trained specifically on weather data that identified irrelevant, neutral, positive, and negative tweets. The obtained tweets and their related atmospheric data were analyzed to establish comfort ranges. The tweets' responses to effective temperature were very similar to those obtained in previous studies, although the peak of comfort is shifted towards the cold stress. Similarly, the tweets' responses to the thermohygrometric index were alike to previous results, but the peak of comfort is shifted towards the heat stress. Regarding the single weather variables under study, the obtained comfort ranges are similar to the ones found in previous research; in particular, the temperature comfort range matches perfectly at 20-22 °C. Therefore, it was concluded that tweets can be used to assess HCOS; not only are the results of this methodology comparable to results obtained in previous studies, but the procedure itself also shows new features and unexpected future applications.