A probabilistic methodology to reconstruct biodiversity from implicit interactions with indicator species along latitudinal co-occurrence networks

With the ongoing decline in biodiversity, there is a need for efficient field monitoring techniques. Indicator species (IS) emerged as a promising tool to monitor diversity because their presence indicates a maximum number of conditionally co-occurring species. Accordingly, there is empirical evidence that few IS could predict local richness. However, species richness is often insufficient to characterize biodiversity. We aim to assess the effectiveness of IS for biodiversity reconstruction based on their co-occurrence with other species. We develop probabilistic models that use IS to predict the occurrence probability of co-occurring species. We predict the occurrence of species based on (1) their conditional occurrence probability with IS and (2) the occurrence probability of IS. We test the approach with field observations of birds in the Côte-Nord region of Québec. First, we identify climate networks (based on temperature and precipitation) over which bird associations remain relatively stable to identify the most locally relevant IS. We use four network-based methods to select the IS according to betweenness, betweenness-closeness centralities, and positive and negative co-occurrences. From co-occurrence networks, we conclude that the latitudinal climate gradient impacts the nature of both biotic interactions and IS composition. Indeed, the proportion of negative links, which reflect competition and avoidance liaisons, was about 25.2% in the southern network, whereas it was 13.2% in the northern network. Moreover, almost a complete turnover in the composition of the IS was observed between the northern and southern networks, with only two common IS among 17. Regarding the effectiveness in the reconstruction of assemblages occurrence, we observed a strong negative correlation ( r ≤ − 0.75) between the percentage of sites occupied and the dissimilarity between the original and the estimated occurrences for a given species. More precisely, species must be present in more than 25% and 33% of northern and southern sites to recover well from its co-occurrence with IS. Depending on climate clusters, our approach enables us to recover the occurrence up to 100% and 70% of species present in more than 20% of sites of northern and southern groups, respectively. However, the accuracy decreases to 9% and 12% of northern and southern groups, respectively, when we include less-present species. The higher success at the north sites reflects the lower species richness observed at higher latitudes. In conclusion, our method demonstrates that it is possible to predict local species assemblages based on the occurrence and absence of IS. Nevertheless, the relatively low success of less present species illustrates the need for further theoretical development to reconstruct biodiversity, mainly to recover the occurrence of rare species. ### Competing Interest Statement The authors have declared no competing interest.