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Camera trapping networks have the potential to monitor wildlife diversity at large scales. However, their efficacy in detecting different species varies, leading to considerable disparities in population density estimates. Furthermore, species of different trophic levels and body sizes naturally occur at different densities, challenging the evenness assumptions inherent in conventional diversity indices. Here we present a novel index: Fractional Richness, which is specifically designed for application in extensive camera trap networks. The index addresses situations where evenness is uninformative, for example in communities characterized by multiple trophic levels, diverse body sizes, variable population densities, or other complications. To determine the effectiveness of our Fractional Richness index, we modeled spatial patterns of Shannon diversity, species richness, and Fractional Richness for two wildlife communities in Wisconsin USA to quantitatively measure which index best reflected ecologically relevant landscape patterns. One community was much more uneven than the other, with detection rates ranging across three orders of magnitude. The more even community could be modeled accurately with both Shannon diversity and Fractional Richness, but the highly uneven community could only be modeled accurately with Fractional Richness. Maximum population density varies by species, and most wildlife survey methods are not equally capable of detecting all species. In communities with both high and low-density species, or when detectability varies, evenness may not be the most informative measure. In these situations, Fractional Richness may be a more suitable index.