Understanding species-habitat relationships for endangered species is critical for their conservation. However, many studies have limited value for conservation because they fail to account for habitat associations at multiple spatial scales, anthropogenic variables, and imperfect detection. We addressed these three limitations by developing models for an endangered wetland bird, Yuma Ridgway's rail (Rallus obsoletus yumanensis), that examined how the spatial scale of environmental variables, inclusion of anthropogenic disturbance variables, and accounting for imperfect detection in validation data influenced model performance. These models identified associations between environmental variables and occupancy. We used bird survey and spatial environmental data at 2473 locations throughout the species' U.S. range to create and validate occupancy models and produce predictive maps of occupancy. We compared habitat-based models at three spatial scales (100, 224, and 500 m radii buffers) with and without anthropogenic disturbance variables using validation data adjusted for imperfect detection and an unadjusted validation dataset that ignored imperfect detection. The inclusion of anthropogenic disturbance variables improved the performance of habitat models at all three spatial scales, and the 224-m-scale model performed best. All models exhibited greater predictive ability when imperfect detection was incorporated into validation data. Yuma Ridgway's rail occupancy was negatively associated with ephemeral and slow-moving riverine features and high-intensity anthropogenic development, and positively associated with emergent vegetation, agriculture, and low-intensity development. Our modeling approach accounts for common limitations in modeling species-habitat relationships and creating predictive maps of occupancy probability and, therefore, provides a useful framework for other species.

Landslide susceptibility maps are useful for spatial decision-making to minimize the lossof lives and properties. There are many studies related to the development of landslidesusceptibility maps using various methods such as Analytic Hierarchy Process, Weight ofEvidence and Logistic Regression. Commonly, the geospatial data required for such analysis(such as land cover and soil type maps) are only locally available and pertinent to smallcase studies. Transferable and scalable approaches utilizing publicly available, large scaledatasets (ie., global or continental) are necessary to develop susceptibility maps in areaswhere local data is not available or when large-scale analysis is required. To develop suchapproaches, a systematic comparison between locally available, fine resolution, and largerscale, openly available but coarser resolution datasets is essential. The objective of this study isto investigate the efficiency of globally available public data for landslide susceptibility mappingby comparing it with the performance of the data provided from local institutions. For this purpose, the Göta river valley in Sweden and the country of Rwanda were selectedas study areas. Göta river valley was used for the comparison of local and open data.While Rwanda was used as a study area to ensure the efficiency of open data analysis andtransferability of the framework. The selected landslide impact factors for this study are;elevation, slope, soil type, land cover, precipitation, lithology, distance to roads, and distanceto drainage network. Landslide susceptibility maps were prepared by using the state-of-the-artFrequency Ratio method. The validation results using the prediction rate curve technique show92.9%, 90.2%, and 83.1% area under curve values for local and open data analyses of Göta rivervalley and open data analysis of Rwanda country, respectively. The results show that globallyavailable open data demonstrate strong potential for landslide susceptibility mapping whenhigh-resolution local data are not available.