Monitoring wildlife is essential for ecology and especially in light of the increasing human impact on ecosystems. Camera traps have emerged as habitat-centric sensors enabling the study of wildlife-environment interactions at scale with minimal disturbance. While computer vision models are becoming more powerful for general video understanding tasks, they struggle comparatively with camera trap videos. This gap in terms of performance and applicability can be partly attributed to the lack of annotated video datasets. To advance research in wild animal behavior monitoring we present MammAlps, a multimodal and multi-view dataset of wildlife behavior monitoring from 9 camera-traps in the Swiss National Park. MammAlps contains over 14 hours of video with audio, 2D segmentation maps and 8.5 hours of individual tracks densely labeled for species and behavior. Behaviors were annotated at two levels of complexity: actions representing simple behaviors and high-level activities. Based on 6,135 single animal clips, we propose the first hierarchical and multimodal animal behavior recognition benchmark using audio, video and reference scene segmentation maps as inputs. To enable future ecology research, we also propose a second benchmark aiming at identifying activities, species, number of individuals and meteorological conditions from 397 multi-view and long-term ecological events, including false positive triggers. We advocate that both tasks are complementary and contribute to bridging the gap between machine learning and ecology. Code and data will be made accessible.