More species-rich communities are often assumed to contain more specialist species, typically characterized by narrower niche breadths or smaller ranges. Stronger interspecific competition in species-rich communities is thought to be a key mechanism explaining these patterns. Yet, the relationship between richness and specialization has so far only been studied for a few taxa, and characterizing the effects of interspecific competition on species niches and distributions is challenging. Thus, it remains unclear how general richness-specialization relationships are. Here, we assess relationships between specialization and interspecific competition along richness gradients of bats across four understudied global biodiversity hotspots. Using a novel, integrated species distribution modeling approach that combines expert range maps and occurrence records of 49 bat species, we produced fine-scale distribution and species richness maps, allowing us to assess environmental niche breadth and range sizes. Further, contrasting potential ranges obtained from traditional distribution models with realized ranges obtained through the integration of expert ranges, we assessed range filling and derived indicators of geographic exclusion that characterize how interspecific competition is limiting species' ranges. Our results highlight that the narrowest niche breadths and strongest geographic exclusion occur in species-poor, not species-rich bat communities, in contrast to what was found for other taxa. While niche breadth peaked at intermediate richness, range sizes decreased continuously with richness. These findings show that increasing bat species richness is not closely linked to environmental specialization across the entire richness gradient and that decreasing range sizes in species-rich communities could be driven by the number of interacting species, rather than by environmental specialization or individually stronger interactions. Our study shows how innovative distribution modeling approaches can shed new light on the interplay of species richness, interspecific competition, and community structure. More generally, our findings caution against generalizing relationships between richness and specialization across taxa and geographies.

Monitoring is a prerequisite for evidence-based wildlife management, yet conventional monitoring approaches are often ineffective for species occurring at low densities. However, some species such as large mammals are often observed by lay people and this information can be leveraged through citizen science monitoring schemes. Assessing the quantity, quality, and potential biases of such data sources is crucial before making inferences at scale. For Eurasian moose (Alces alces), a species currently reoccurring in north-eastern Germany in low numbers, we compared three different citizen science tools: a mail/email report system, a smartphone application, and a webpage. Among these monitoring tools, the mail/email report system yielded the greatest number of moose reports in absolute and in standardized (corrected for time effort) terms. The reported moose were predominantly identified as single, adult, male individuals, and reports occurred mostly during late summer. Overlaying citizen science data with independently generated habitat suitability and connectivity maps showed that members of the public detected moose in suitable habitats but not necessarily in movement corridors. Also, moose detections were often recorded near roads, suggestive of spatial bias in sampling effort. Our results suggest that citizen science-based data collection can be facilitated by brief, intuitive digital reporting systems. However, inference from the resulting data can be limited due to unquantified and possibly biased sampling effort. To overcome these challenges, we offer specific recommendations such as engaging outdoor enthusiasts in suitable moose habitats, for improving quantity, quality and analysis of citizen science-based data for making robust inferences about wildlife populations.