We have entered the age of ecological macro and micromonitoring, as continued developments in tagging and remote sensing technologies provide ecologists with vast volumes of data at unprecedented combinations of scale and resolution. Yet although data collection is increasingly unbound, leveraging it to improve conservation and management implementation still poses challenges. Data volume often forces researchers to consider trade-offs between veracity (i.e., the accuracy of the data) and processing speed (i.e., how quickly data can be used). The extent and resolution of new sampling techniques can uncover new ecological patterns, but the novelty of such patterns can make it difficult to conceptualize useful models to describe them. Here, I try to take steps towards surmounting some of these issues. Chapters 1 and 2 focus on issues of data veracity. Although measurement and classification error are ubiquitous in ecological data, these problems have become more visible as researchers increasingly depend upon algorithms or recruited volunteer scientists to perform data collection and classification tasks. Chapter 1 describes a general framework to guide researchers undertaking data quality assessments and implementing remediation actions that is rooted in ecological inference rather than error incidence. Chapter 2 focuses on expanding the statistical tool-kit that ecologists can use to account for misclassified detection/non-detection data, and demonstrates that previously developed approaches focusing on occupancy estimation are easily extensible to essentially any parametric model class reliant on species occurrence data. Chapters 3 and 4 focus on longer-standing ecological questions, but bringing new seasonal resolution to bare. Chapter 3 focuses on quantifying deer behavioral responses to predation risk, and seeks to disentangle competing hypotheses for how such responses are structured. Deer respond to proximal measures of potential wolf predation risk in ways that might be expected to have cascading vegetation effects in some environmental contexts, but not others. In particular, deer responses were strongly mediated by seasonal environmental variables, suggesting a potential ‘phenology of fear’. Chapter 4 seeks to delineate wildlife communities and uncover the primary environmental factors that structure their occurrences. Snow appears to be a particularly powerful driver of species distributions, and wildlife responses to changes in snow-depth and vegetation greenness across the year drive distinct seasonal variation in patterns of species richness: such temporal variation (or partitioning of the “seasonal” niche) may play a key role in maintaining community diversity.