Introduction
Loss of biodiversity is a current global threat that is being exacerbated by climate change (Habibullah et al. 2022). This is particularly concerning due to the consequences of species loss on ecosystem function (Duffy 2009). Since the early 19th century, the average abundance of native species in major terrestrial habitats has decreased by at least 20% and this is expected to accelerate due to anthropogenic factors (Watson et al. 2019). The UK is one of the most nature-depleted countries in the world, with only half of its native biodiversity remaining, compromising the health of ecosystems (Newbold et al. 2016, Natural History Museum 2021). Several UK government plans to conserve and restore species and habitats have been introduced as the long-term survival of many species, habitats and ecosystems is dependent on proactive conservation (Scottish Government, 2013; Welsh Government 2015; Defra, 2018; Chapman et al. 2019; Gann et al. 2019; Defra, 2023).
One of the most well-known and accessible conservation assessment methods is the IUCN Red List of Threatened Species which classifies species’ conservation threat statuses (IUCN 2020). It comprises individual conservation assessments for many species which aim to guide conservation management decisions. Although genetic diversity has been recognized as a fundamental component of biodiversity by the IUCN (McNeely et al. 1990), it has never been explicitly or systematically incorporated into IUCN Red List assessments. This limitation has drawn substantial criticism, as these assessments primarily focus on species-level metrics, such as population size and extinction risk and do not consider genetic diversity (e.g., Rivers et al. 2014), while largely neglecting the genetic variation essential for species' adaptive potential and resilience to environmental change (Hoban et al. 2020; Laikre et al. 2020). Genetic diversity underpins key evolutionary mechanisms (genetic drift, gene flow, natural selection, and mutation) by supplying the variation necessary for populations to adapt and persist in changing environments (Booy et al. 2000; Hoffman & Srgò 2011; Chapman et al. 2019). Higher genetic diversity underpins long-term persistence of populations as it is linked to increased heterozygosity and decreased inbreeding which have both been associated with increased fitness (Reed and Frankham, 2003). Genetic diversity has significant ecological consequences and has substantial effects on productivity and population recovery (Hughes et al. 2008). In the face of the climate change and biodiversity crises, maintaining genetic diversity is essential to the long-term survival of populations and should therefore form a fundamental part of conservation management strategies.
Threatened species often have small, fragmented populations and are therefore more likely to suffer the negative effects associated with low genetic diversity. Long-term small population size can lead to inbreeding, resulting in the accumulation of deleterious mutations (Liu et al. 2021). Furthermore, small populations are vulnerable to high levels of genetic drift that can decrease genetic diversity and increase extinction risk (Reed & Frankham 2003; Keyghobadi et al. 2021). The implementation of risk management strategies and frameworks can help to drastically improve the outcomes of conservation management strategies and enable conservation practitioners to make more informed decisions (Byrne et al. 2011). Mixing populations is sometimes suggested as a management strategy to increase genetic diversity or introduce adaptive traits into a population (Weeks et al. 2011). However, if genetic diversity is not carefully considered, mixing genetic material from genetically different populations can have negative effects such as outbreeding depression and maladaptation (Broadhurst et al. 2008) which can reduce the viability of populations and the likelihood of the success of conservation management (Furlan et al., 2020). Outbreeding depression can cause decreased offspring fitness when crossing with genetically different lineages (Weeks et al. 2011). It can occur when translocating individuals or mixing populations belonging to different species, with chromosomal differences, not having exchanged gene flow in 500 years,) or when populations are adapted to different environments (Frankham et al 2011; Weeks et al 2011). Nevertheless, mixing populations can be a valuable tool to avoid negative impacts of inbreeding or bottlenecks if genetic differences and diversity are carefully considered (Broadhurst et al. 2008; Byrne et al. 2011).
The inclusion of genetic diversity in conservation management has been recommended to conservation practitioners previously and is often incorporated in programmes such as ex-situ seed banking (Booy et al. 2000; León-Lobos et al. 2012; Hoban et al. 2013; Chapman et al. 2019; Ray & Bordolui 2021). However, it is not always prioritised in conservation management decisions (Kahilainen et al. 2014). This is unsurprising, given that its inclusion requires specific scientific information resulting from complex genetic analysis which for some species can be impossible to gain for reasons such as the lack of funding and access to genetic material (Theissinger et al. 2023). Incorporating the conservation biology principles of redundancy, representation, and resilience can also support the protection of genetic diversity, even in the absence of direct genetic data. These principles are central to the Species Status Assessment (SSA) process, which evaluates species' viability by considering factors like distribution, population structure, and potential environmental changes. By focusing on broader ecological and population-level factors, the SSA can guide conservation decisions that protect genetic diversity through strategic conservation efforts, even when specific genetic data are difficult to obtain (Smith et al. 2018).
Frameworks that prioritise genetics often require expensive materials and time-consuming work to provide estimates of genetic diversity, population differentiation, gene flow, hybridisation and inbreeding to inform decisions (Hoffman et al. 2015; Ottewell et al. 2016). Several models have been proposed which operate without the need for, or in the absence of, genetic data. These have been developed in the context of seed sourcing based on environmental differences between populations (Breed et al. 2013) and life-history traits and genetic factors (Walker et al. 2004; Byrne et al. 2011; Ottewell et al. 2016). A more recent study also proposed a “reactive/proactive” approach based on the synthesis of the risks and benefits of using local seed in revegetation programmes (Török et al. 2024). However, these approaches are often difficult to apply in real-world situations and depend on the availability of applicable data, which is often difficult to obtain. Neaves (2019) proposed a risk assessment framework which, in contrast to the model proposed by Walker et al. (2004), utilises only life-history traits to estimate genetic diversity, genetic differentiation and inbreeding metrics. Neaves’ (2019) framework then uses these metrics to evaluate the risks of sampling and donor selection for ecological restoration based on the estimated effects of mixing populations. This approach has previously been successfully applied to UK trees and shrubs to evaluate knowledge gaps related to UK native tree genetics and has since been used to develop policy recommendations and funding for native tree seed supply (Gargiulo 2019; Sustainable Seed Sourcing Project, 2023). For example, Gargiulo (2019) used Neave’s framework in 44 species and The Tree Seed Species Strategies (2023) includes recommendations for 17 tree species, such as establishing clonal seed orchards for aspen (Populus tremula L.) or recommending sampling strategies to represent the distribution range (e.g., hazel (Corylus avellana L.), elder (Sambucus nigra L.)). Ottewell et al. (2016) proposed a framework which relies on genetic data to estimate levels of genetic differentiation, genetic diversity and inbreeding metrics. This framework is further utilised to deliver comprehensive management recommendations for conservation practitioners. Such an approach has been applied to threatened species like Androsace cantabrica (Losa & P. Monts.) Kress, where conservation units were defined based on genetic structure. Specific conservation strategies, including translocations to boost genetic diversity and habitat threat management, were recommended for each genetic group (Liang et al. 2024). Combining Neaves’ approach, which infers genetic metrics without requiring genetic data, with Ottewell’s framework, which uses genetic data to guide conservation actions for threatened species, could provide a flexible and cost-effective strategy. This integration would allow practitioners to make informed decisions in scenarios where genetic data is unavailable or impractical to obtain, while still enabling precise and targeted management when such data is accessible, ultimately enhancing the adaptability and impact of conservation and restoration efforts. Here, we propose a framework for species conservation assessments that prioritise population genetic parameters, but do not require genetic testing and analysis. Our framework builds on the approaches of Neaves (2019) and Ottewell et al. (2016) by combining the strengths of both methodologies. While Neaves’ framework estimates genetic parameters from life history traits, it does not provide management strategies. Conversely, Ottewell’s genetic assessment framework relies heavily on genetic data, which is often unavailable. Our combined framework uses life history traits to genetic essential biodiversity variables (EBVs; Hoban et al. 2022): genetic diversity, differentiation, and inbreeding potential (following Neaves) and integrates these estimates to provide actionable conservation management recommendations (adapted from Ottewell). The Group on Earth Observations Biodiversity Observation Network (GEO BON) developed EBVs as metrics to interpret biodiversity data from a range of sources (Pereira et al. 2013). Genetic EBVs, proposed by Hoban et al. (2022), have been suggested as a good measure to summarise and compare biodiversity among species, especially for conservation policy (Schmidt et al. 2023). It is also possible for some genetic EBVs to be estimated without molecular data (O’Brien et al. 2022). In our rationale, we estimated genetic EBV values based on life history traits, similarly to Neaves’ (2019) estimations of genetic diversity and differentiation, and we also included the inbreeding EBV (Hoban et al. 2022). This allowed us to combine these trait values with the conservation management strategies outlined in Ottewell et al. (2016) which also uses similar metrics. These three approaches combined provide a comprehensive, applicable framework that provides conservation strategy recommendations as well as risks of sampling and donor selection as in Neaves (2019).
We use a selection of 52 native or archaeophyte UK plant species of conservation and ecological restoration interest to present this framework and its application for conservation practitioners.