Assessing the value of environmental DNA into conservation planning: A case study of freshwater bivalves in France

Environmental DNA (eDNA) is emerging as a valuable tool for generating standardised biodiversity data. Whether used alone or alongside conventional surveys, it is expected that eDNA data can improve conservation planning, but this remains largely untested. Here, we assessed the value of eDNA data for making more robust conservation prioritisation decisions, by comparing errors made when identifying priority areas based on conventional data, eDNA or a combination of both. We collected distribution data for 29 freshwater bivalve species in France from both a conventional source (public databases) and field eDNA surveys. We then used Marxan, a spatial prioritisation tool, to assess how conventional versus eDNA data can influence conservation decisions. Data from both sources were used to model species distributions, considering either all available data (full dataset), eDNA data only (eDNA dataset), conventional data only (conventional dataset), or varying proportions of conventional and eDNA data (mixed datasets: 25/75; 50/50; 75/25). We compared the performance of conservation planning solutions derived from the different datasets against the full dataset, which served as the best approximation to the species' true distributions. For each dataset, we assessed the percentage of species targets met (effectiveness), the efficiency of the conservation solutions, the representation error rates compared to the best attainable information (omission and commission errors), and the overlap between each scenario and the full dataset. The inclusion of eDNA data allowed us to model the distribution of more species, compared to conventional data. However, when using eDNA data alone, fewer species achieved the targets and solutions were less efficient, requiring more areas to be selected. Using either conventional or eDNA data alone was associated with higher commission and omission errors, respectively. Overall, integrating eDNA data with conventional datasets outperformed using conventional data alone, improving the efficiency of conservation solutions.