Disparity between ecological and political timeframes for species conservation targets

The rapid decline in biodiversity driven by human activities over recent decades (Chase 2014; Pimm et al. 2014) has resulted in losses similar to those last seen during mass-extinction events recorded over geological time periods (Ceballos et al. 2017; Drira et al. 2019). In recognition of this, in 2010 the signatory countries of the Convention on Biological Diversity (CBD) adopted 20 ‘Aichi Biodiversity Targets’ as part of the Strategic Plan for Biodiversity 2011–2020, with the aim of halting biodiversity loss by 2020 (CBD 2010). Among these, Aichi Target 12 addresses species conservation specifically, stating that “By 2020 the extinction of known threatened species has been prevented and their conservation status, particularly of those most in decline, has been improved and sustained” (CBD 2011). Despite increasing policy and management responses, at the conclusion of the Aichi Targets, the evidence shows that we have failed to halt biodiversity loss, and species extinction risk continues to increase (IPBES 2019; CBD Secretariat 2020). As Parties to the CBD now negotiate a new post-2020 global biodiversity framework (CBD 2020) it is essential that we learn lessons from the Aichi Targets, in order that new targets will promote greater progress in conserving biodiversity over the coming decades.

The formulation of targets is considered an important determinant of successful implementation (Butchart et al. 2016), and the Aichi Targets have been criticised for being arbitrary, ambiguous, overly complex, difficult to quantify and unachievable (Adenle et al. 2015; Nicholson et al. 2018). Target 12 however, is regarded as operational and quantifiable (Butchart et al. 2016; Hagerman and Pelai 2016; Montoya et al. 2018). Furthermore, while Target 12 may have been ambitious, a recent study found that progress towards achieving global conservation targets was not related to the ambitiousness of the target (Green et al. 2019). One of the most fundamental questions regarding the appropriateness of a target, however, is whether the target can realistically be met within the timeframe set; in other words, is the target achievable?

Progress towards Aichi Target 12 is assessed by quantifying species conservation status using the IUCN Red List of Threatened Species, which is the most comprehensive resource detailing the conservation status of taxa globally (Rodrigues et al. 2006). The IUCN Red List categorises species extinction risk based on a range of population or distribution criteria (Mace et al. 2008) and the Red Listing process is deeply embedded within CBD approaches to species conservation (e.g. CBD 2016). For an improvement in species conservation status to be detected, species must show a substantial enough improvement in population and/or distribution trends to warrant a change in Red List category. Such changes in response to conservation action may take years or even decades to realise, particularly in long lived species (Jones et al. 2016; Kuussaari et al. 2009). Thus a critical question concerning whether Target 12 was achievable, is whether the ten-year time frame allocated to achieve Target 12 was long enough to detect improvements in the conservation status of species through assessment on the IUCN Red List.

Appropriate policy timeframes are important not only for understanding progress–or lack thereof–towards existing targets, but also for informing future target setting. Parties to the CBD are now focussed on the 2050 Vision of ‘living in harmony with nature’, and are negotiating milestones towards this vision, including 2030 goals that will almost certainly include a species target (CBD 2020). The scientific community therefore has an important opportunity to inform ambitious yet realistic species targets over multiple timeframes. Previous work has demonstrated how policy and decision making could be informed by projecting changes in species conservation status under alternative management scenarios (e.g. Nicholson et al. 2012; Costelloe et al. 2015; Visconti et al. 2016). Such studies projected impacts of alternative scenarios on the Red List Index (RLI), which is derived from the Red List and quantifies overall change in global species extinction risk. The CBD Secretariat regards the RLI to be the most relevant indicator for measuring progress towards Target 12 (CBD 2011), yet the RLI is a relatively coarse measure that would not be expected to change rapidly (Jones et al. 2011). Furthermore, focussing on overall change in RLI masks variation among species in their responses to conservation action. Successful conservation would ideally see an improvement across all species, however some species, such as those that are long-lived, may be slow to respond to change due to traits such as low fecundity (Watts et al. 2020). It has proposed that post-2020, progress towards species conservation targets could be measured using a suite of indicators, including the percentage of threatened species that are improving in status according to the Red List (CBD 2021). Projecting changes in the conservation status of individual species based on species-specific population growth rates would thus allow us to test whether improvements in conservation status can be realistically achieved for all species, within a given timeframe.

In this study, we investigate the potential mismatch between the timescale of Aichi Target 12 and the timescale of potential measurable improvement in species conservation status. Specifically, we tested whether the population growth rates of a range of threatened bird and mammal species could lead to sufficient increases in population size for the species to be down-listed on the IUCN Red List within a ten-year timeframe. Projecting changes in species population size, and consequent changes in Red List category, allows us to measure improvement in conservation status at the individual species level. We focussed on population growth rates because it is generally a greater challenge to improve species’ conservation status through an increase in population size (as required for species with very small population sizes) compared to simply halting decline (as required for species that are threatened due to declining population and/or distribution). We obtained estimates of the number of mature individuals from the IUCN Red List and conducted a literature search to identify species’ population growth rates. We then used population models to determine the time it would take to detect an improvement in species Red List status, and assessed whether this was achievable within the ten-year timeframe of Aichi Target 12.

Our study used simple population models to test whether the ten-year timeframe of Aichi Target 12 was long enough for a selection of threatened species to achieve down-listing on the IUCN Red List. We predicted population growth for a total of 57 species of birds and mammals out of 119 species documented as threatened due to small population sizes on the IUCN Red List. We found that in the best-case scenario, 39/42 birds (93%) and 12/15 mammals (80%) could be expected to show the population increase required to achieve down-listing by one Red List category within a ten-year timeframe. In contrast, under the worst case scenario, 28/42 birds (67%) and 6/15 mammals (40%) were predicted to take more than ten years to reach the population threshold, indicating that even when effective conservation measures are in place, the ten-year timeframe of the Aichi Targets may be too short for the impact of conservation action to be detected as a change in Red List category for some threatened species.

To achieve down-listing on the Red List within the timeframe of the Aichi Targets, species’ population sizes would have to cross the extinction risk category threshold within the first five years. The predicted ability of species to do so varied depending on the estimate of population growth rate and on the starting population size. Many species had an estimated maximum population size that was close to the down-listing threshold, meaning that this threshold could be reached within only a few years. Yet some of the same species were predicted to take > 20 years to reach the threshold when the estimated minimum population size was used. This variation in outcomes reflects substantial uncertainty in our knowledge of species’ population sizes, as well as variation in reported growth rates. The long times taken for many species to reach the threshold under some scenarios also emphasises the coarse nature of Red List categories (Jones et al. 2011); downlisting may not be achieved for decades, despite consistent population increases. These results suggest that there is a disparity between the ecological timeframes required for species to show a reduction in extinction risk, and the political timeframes over which such ecological change is expected to be achieved. This disparity has implications for the achievability of future species conservation targets. As the post-2020 global biodiversity framework is shaped this year, we suggest that careful consideration should be given to both the achievability of time-bound targets and the temporal sensitivity of the methods used to measure progress towards these targets.

With the current orientation of the CBD towards their 2050 vision and the ongoing negotiations of milestones towards this vision, there is the opportunity for ecological modelling to inform realistic policy targets. Scenario modelling can be used to project the impacts of alternative policy and management options on species conservation outcomes (e.g. Nicholson et al. 2012; Costelloe et al. 2015; Visconti et al. 2016). To date, such work has modelled overall changes in species extinction risk and has not taken account of individual species population growth rates, which may be low particularly for long-lived species. We suggest that the accuracy and utility of scenario modelling could be enhanced by including species-specific parameters, such as expected population growth rates. Moreover, models that account for species-specific traits could be used to set realistic, incremental milestones towards the 2050 vision, for example by setting realistic targets for the proportion of species that should have improved conservation status per decade (such as suggested by Butchart et al. 2019). Such detailed information could also be used to identify ‘quick wins’ that could boost political and public morale and provide impetus for further conservation action and investment (McAfee et al. 2019). In particular, models could be used to identify the species for which conservation action must be implemented early if the species are to recover sufficiently to meet targets later on.

The ability to develop models that incorporate species-specific parameters is likely to be limited by data availability. Despite identifying 119 species of mammals and birds that had estimated population sizes and were listed as threatened under criterion D, we found species-specific population growth rate estimates in the scientific literature for only fourteen of these species. Given that birds and mammals are among the most studied taxa (Clark and May 2002), we would expect even greater data limitations in other taxonomic groups. Lack of species-specific data can be overcome to some extent; we found estimates for species within the same genus for a further 43 species. Population projection models are increasingly being used to identify effective management strategies for threatened species (Alemayehu 2013; Earl 2019; Hartmann et al. 2017; Hegg et al. 2013), and as such, the availability of demographic data is likely to continue to improve, particularly for known threatened species.

Our study necessarily made some broad assumptions. The population models assumed that effective conservation was implemented immediately following the adoption of Aichi Target 12 and that this action produced an exponential population growth. From a political perspective, this assumption is extremely optimistic, as conservation responses to biodiversity continue to be offset by the rising pressures on biodiversity (Johnson et al. 2017). From an ecological perspective, this assumption means that we did not consider demographic, environmental or genetic stochasticity or the effects natural catastrophes may have on population responses to conservation action (Saunders et al. 2018; Shaffer 1981). Our models also did not consider potential time-lags in the responses of species’ populations to conservation action (Watts et al. 2020). As a result, our predictions are highly optimistic, and we would expect that more nuanced population models would predict substantially longer times to achieve reductions in species extinction risk.

A further assumption our study made was that species listed under Criterion D (threatened due to small population size) had historically larger population sizes. Although 66% of our study species occurred on islands, where population sizes are more likely to be naturally small, there was evidence in the Red List accounts that 52% of islands species and 70% of mainland species historically had larger population sizes. A further 25% of island and 18% of mainland species had insufficient data to assess whether population sizes were larger in the past. We therefore consider it a reasonable assumption that the majority of our study species would naturally occur at larger population sizes pre-human impact. While for the majority of threatened species, halting population and/or distribution declines would accomplish down-listing on the Red List, species at risk of extinction due to small population sizes merit special attention, as these species are more prone to inbreeding effects and more at risk of stochastic events. They are therefore likely to require targeted conservation action, such as translocations or population augmentation, in order to increase population sizes. They are also likely to be the species for which the inclusion of species-specific parameters is most important in scenario modelling and conservation planning.

Our study demonstrates that the timeframe of Target 12 was too short to detect an improvement in Red List status for all threatened species. This implies that Target 12 was overly ambitious, as the quantitative elements of a target should be based on scientific evidence to ensure that the target is well founded (Nicholson et al. 2018). However, global conservation target setting requires a careful balance of conservation ambition, political reality and scientific evidence. We therefore do not suggest a reduction in ambition, but instead suggest that quantitative analyses such as the one we provide here should be used to set realistic milestone targets towards the 2050 vision. Furthermore, consideration should be given to the temporal sensitivity of indicators used to measure progress towards timebound targets (Collen and Nicholson 2014). Changes in Red List category–and ultimately the trend in Red List Index–provide a long-term global indicator of species conservation status. These global indicators could be supplemented with more local and temporally sensitive measures, such changes in the proportion of species with expanding distributions or increasing population trends, and the proportion of species that showed declines before 2020 but have since stabilised (Butchart et al. 2019). Monitoring the population and distribution changes that can be expected to eventually drive changes in species extinction risk provides shorter-term evidence of the effectiveness of implemented action. Such evidence may be important to allow biodiversity change to be measured over time periods of relevance to policy makers.

Appropriate conservation action can produce population and/or distribution increases, and consequently achieve species down-listing on the Red List (Jones et al. 2016). Moreover, intensive conservation efforts can pull species back from the brink of extinction (Bolam et al. 2020). There is therefore good reason to be optimistic and ambitious when setting species conservation targets in the post-2020 global biodiversity framework. However, if we are to maintain political and societal motivation, targets need to be realistic and measures of progress towards targets need to be able to detect positive conservation impact over short time periods of relevance to policy makers. Analyses such as those presented here can be used to identify where complementary indicators that respond over short timeframes need to be adopted, and set realistic, science-based milestones towards the CBD’s 2050 vision.