Geospatial data and tools evolve as new technologies are developed and landscape change occurs over time. As a result, these data may become outdated and inadequate for supporting critical habitat-related work across the international boundary in the Sonoran and Mojave Deserts Bird Conservation Region (BCR 33) due to the area’s complex vegetation communities and the discontinuity in data availability across the United States (US) and Mexico (MX) border. This research aimed to produce the first 30 m continuous land cover map of BCR 33 by prototyping new methods for desert vegetation classification using the Random Forest (RF) machine learning (ML) method. The developed RF classification model utilized multitemporal Landsat 8 Operational Land Imager spectral and vegetation index data from the period of 2013–2020, and phenology metrics tailored to capture the unique growing seasons of desert vegetation. Our RF model achieved an overall classification F-score of 0.80 and an overall accuracy of 91.68%. Our results portrayed the vegetation cover at a much finer resolution than existing land cover maps from the US and MX portions of the study area, allowing for the separation and identification of smaller habitat pockets, including riparian communities, which are critically important for desert wildlife and are often misclassified or nonexistent in current maps. This early prototyping effort serves as a proof of concept for the ML and data fusion methods that will be used to generate the final high-resolution land cover map of the entire BCR 33 region.

Recreation specialization is a framework that can be used to explain the variation among outdoor recreationists’ preferences, attitudes, and behaviors. Recreation specialization has been operationalized using several approaches, including summative indices, cluster analysis, and self-classification categorical measures. Although these approaches measure the multiple dimensions of the framework, they may not reflect the relative contribution of the dimensions to individuals’ degree of engagement. We illustrate an approach that uses second-order confirmatory factor analysis (CFA) factor scores as weights to determine a person’s degree of recreation specialization and compares the CFA-based results to those derived from cluster analysis. This approach permits the use of a broader set of statistical tests when compared to categorical specialization measures and provides information about the distribution of responses. Data were collected from an online survey of eBird registrants from the United States.

Arachnology focuses on all aspects of arachnology, including discussions of biology and ecology, descriptions of new species, and recent discoveries.

Canadian and American waterfowl hunters were surveyed to identify their hunting trip preferences. Respondents were individuals that were now participating or had participated in waterfowl hunting, and most had hunted the majority of the last five years. We identified four latent classes of waterfowl hunters that varied in their preferences for harvest, access effort, length of travel, quantity of waterfowl seen, and the potential for interference/competition. We found a diminishing return associated with the number of waterfowl harvested, and that ‘devoted’ and ‘local’ hunters did not perceive appreciable benefit from harvesting more birds beyond harvesting a single bird. Results highlight the importance of not only considering population size, but also the location of habitat for people and waterfowl. Our results provide waterfowl managers important insights into the heterogeneity of North American waterfowl hunters by highlighting differences in priorities for waterfowl hunting trips. Notably, to address this heterogeneity, managers could consider the balance of objectives, actions and resources designed to satisfy current waterfowl hunters. Managing access to improve the likelihood that hunters will see and have opportunities to harvest some waterfowl has benefit to hunters.