However, for species with long generation times, it may be beneficial to react in a proactive fashion and prioritize populations that exhibit low vulnerability to protect their global genetic diversity as a reservoir. Prioritization frameworks are often reactive, targeting highly vulnerable populations in need of immediate management actions to avoid reducing genetic diversity and local or global species extinction events. Multiple prioritization frameworks have been developed to provide objective criteria during decision-making processes, and either estimate species extinction risks or prioritize threatened populations and determine the management actions required to support them. The latter is used by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) to define populations or subspecies of wildlife that require protection under the Species at Risk Act (SC 2002, c 29).Ĭonsidering that two populations may be ranked in the same category even when faced with entirely different threats, and the limited financial resources available for conservation biology, prioritizing populations arose as an essential concept to target the most effective conservation actions. Such multiple criteria have been used to categorize species at risk and aid governmental and non-governmental organizations to define conservation units, including: Management Units (MUs, ), Evolutionary Significant Units (ESUs, ) and Designable Units (DUs).
Rule-based methods have been developed by the International Union for Conservation of Nature (IUCN) to categorize populations based on the level of threat they face.
The threats experienced by animal and plant populations require distinct management practices that must consider the economic, social and cultural aspects of the human society living in closest proximity to them. Furthermore, they exhibit greater sensitivity to extirpation, which may inform of the general trend at the species level. Populations show important local adaptive variations that are essential for species adaptation to changing environments. The conservation of units below the species level, commonly populations, is widely used in wildlife management, as it provides critical information to support species survival and evidence of within-species differences. As such, BRIDES offers an interesting complement to node-based metrics and node removal to better understand the global processes at play when addressing population prioritization frameworks.
We show that population graphs can reveal complex gene flow dynamics and population resiliency to local extinction. To our knowledge, this study is the first to apply graph theory for turtle conservation genetics. Contrary to centrality measures focusing on static networks, BRIDES allowed for the analysis of evolving networks. Results showed repeated selection of certain wood turtle populations, which could have not been predicted following only genetic diversity and distinctiveness estimation, node-based metrics and node removal analysis. Our results demonstrate the robustness of the BRIDES selection process for a given scenario, while inconsistencies were observed among node-based metrics. We constructed six population subgraphs and used a stepwise selection algorithm to choose the optimal number of additional nodes, representing different populations, required to maximize network connectivity under different weighting schemes. The main objectives of this study were to (1) introduce a BRIDES selection process to assist conservation biologists in the prioritization of populations, and (2) use different centrality indices and node removal statistics to compare BRIDES results and assess gene flow among wood turtle populations. BRIDES characterizes different types of shortest paths among the nodes of a subgraph and compares the shortest paths among the same nodes in a complete network. Using a wood turtle ( Glyptemys insculpta) population graph, we introduce BRIDES as a new tool to evaluate populations for conservation purpose without focusing solely on individual nodes. Genetic diversity within and among populations is frequently used in prioritization processes to rank populations based on their vulnerability or distinctiveness, however, connectivity and gene flow are rarely considered within these frameworks.
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