Characterising how species respond to its environment is of central interest in ecology. Species-environment relationships (SERs) are studied in many topics, for instance, in community ecology, in species distribution modelling, and to guide conservation or management actions. Statistical models fitting species distribution data (e.g., presence/absence or counts) to environmental data (e.g., temperature) are often used to estimate SERs. Standard models assume that data is representative of the SER. However, data can represent only a partial description of the SER. In this work, we investigated the effects on modelling SERs of three kinds of partially observed data:1) Partially observed response data, e.g., species sampled occurrences may only represent a partial observation of the occupancy status due to missing species present (i.e., imperfect detection).2) Partially observed environmental data, e.g., environmental descriptors may represent averaged conditions at a coarser spatial scale than the one at which SER is studied (i.e., area-to-point spatial misalignment).3) Partially observed relationship, e.g., the gradient of environmental conditions that describe the SER are not entirely surveyed (i.e., truncated gradient).Hierarchical Bayesian Models, allowing multi-species inferences and disentangling ecological from observational processes, have been developed and tested in three case studies, each involving a particular type of partially observed data.In the first case study, we emphasized that even a robust sampling design that involves multiple sampling replicates and detection techniques can lead to species detection probabilities lower than one in an insect community. We then advocated for the use of Multi-Species Occupancy Models to account for imperfect detection in insect studies. In the second case study, we showed how using area-to-point misaligned covariate can flatten SERs estimated by generalized linear models and how fitting a Berkson error model can lower the bias. In the third case study, we developed a hierarchical model that explicitly estimates optimum shifts. By constraining estimated SERs to concave shapes (following ecological theory), the new model improved estimates relative to past methods, especially in the case of truncated gradients.