The study of species environmental niches underpins numerous questions in ecology and evolutionary and has increasing relevance in a rapidly changing world. Environmental niches, characterized from observations of organisms, inform about a species’ specialization in multivariate environment space and help assess their exposure and sensitivity to a changing climate. Environmental niches are also the central concept behind the species distribution models (SDMs) which assess and predict the geographic variation in environmental suitability. Despite the clear role of past evolutionary processes in driving contemporary biodiversity distribution, the assessment of multivariate or n-dimensional (where n is the number of environmental axes) niches in a phylogenetic framework has remained limited and constrained by restrictive assumptions. This hampers important existing and emerging applications, such as assessments of niche conservatism, estimates of species’ adaptive potential under changing climates, and prediction of niches in less-studied parts of the tree of life. Here we introduce a framework that extends SDMs to estimate n-dimensional environmental niches jointly with underlying evolutionary processes. Specifically, we fit the relationship between niche distance and phylogenetic distance as a latent Gaussian Process across all species in a clade. We demonstrate mathematically how the parameters of the Gaussian Process can be linked to existing traditional evolutionary models. Simulations indicate that the approach successfully recovers evolutionary parameters. Applied to two clades of hummingbirds, the presented joint framework uncovers the relationships among species’ niches in phylogenetic space and supports the quantification and hypothesis testing of niche evolution. A key advantage of the presented framework is its joint estimation of the evolutionary process alongside niches directly from species observations with uncertainty propagated to evolutionary model parameters. The proposed approach has the potential to increase the robustness of inference about niche evolution and improve understanding of how the processes of niche formation and evolution interact.

Human activities are driving environmental and climatic changes, affecting the distribution and diversity of species worldwide. Limiting the negative impacts of these activities on wildlife requirestimely knowledge of status and trends in populations over large scales. Camera trapping providesopportunities to simultaneously collect information on several species over large spatio-temporalscales. However, the time required to process large collections of images, the statistical andprogrammatic skills needed to analyze large sets of data, and a general lack of homogeneity in metadata standards hinder the use of camera trapping for local and global conservation. WildlifeInsights (http://wildlifeinsights.org/; WI) is a web platform that promotes and supports the use andsharing of camera-trap data for species conservation and promotes the mobilization of records thatotherwise might be permanently siloed in private data-storage units or lost over time. WI speeds upthe processing of images via an AI model trained to classify >700 species, and automates commonstatistical analysis through a standardized, accessible user interface. It also provides tools to addresscommon issues faced by camera trappers, such as the need of hiding locations of sensitive speciesand removing images of humans, and has a transparent infrastructure to request, share and citedatasets. Although only recently open to the public, the platform already hosts tens of millionsrecords, most of which publicly accessible, from more than 50 countries and 1000 species. Using datashared in WI, we assessed whether information collected using camera traps improved the spatial,temporal, taxonomical, and ecological coverage of many species compared to records available inmore traditional open-access repositories such as GBIF. Birds and mammals, and countries with ahigh proportion of remote areas and biodiversity had the largest increases in coverage. Compared toother traditional methods, camera traps also provided fi ner-resolution temporal information, oftenreplicated over time. Our results showed the importance of sharing camera-trap data for conservationand highlights WI’s role as an invaluable resource to timely inform biodiversity conservation in achanging world