Biology subjects

Clements, C.

Publications and source records attributed to Clements, C..

7 recordsLinked to original sources

bioRxiv2025

A functional Red List Index for monitoring trends in functional diversity

Functional diversity supports ecosystem function and resilience but is in decline due to ongoing extinctions. Despite this, no functional diversity metrics are included in the set of indicators adopted by governments to measure progress towards the Global Biodiversity Framework. Monitoring changes in functional diversity is essential to understand differences between taxonomic and functional diversity trends. The Red List Index (RLI) is a widely used metric in conservation policy, and tracks trends in aggregate extinction risk across sets of species over time. We present a functional Red List Index (fRLI), which weights species by their morphological uniqueness at either local or global scales, to indicate the risk that future extinctions could pose to functional diversity. We applied the fRLI on birds and found higher aggregate extinction risk for both local and global fRLI compared with the standard RLI. Species with higher local uniqueness were more likely to be at higher risk of extinction while we found no difference for globally unique species. Globally or locally unique species were not more likely to show escalation in extinction risk between 1988 and 2024 than less unique species; however, locally unique birds had a lower proportion of their resident range covered by site-based conservation measures, leaving them vulnerable to future decline. The fRLI provides a tool to monitor the impact of extinction risk on functional diversity and could be applied to other taxonomic groups with comprehensive or representative coverage of Red List assessments and trait data.

ecology↗

bioRxiv2025

Threat reduction must be coupled with targeted recovery programmes to conserve global bird diversity

Biodiversity is undergoing severe declines while threats to natural populations continue to escalate worldwide. Ambitious international commitments have been made to preserve biodiversity, with the goal of preventing extinctions and maintaining ecosystem resilience, yet the efficacy of large-scale protection remains unclear. Here we use a trait-based approach to show that global actions - such as the immediate abatement of all threats across at least half of species ranges for [~]10,000 bird species - will only prevent half of projected species extinctions and functional diversity loss attributable to current and future threats in the next 100 years. Nonetheless, targeted recovery programmes prioritizing protection of the 100 most functionally unique threatened birds could avoid 68% of projected functional diversity loss. Actions targeting habitat loss and degradation will prevent the greatest number of species extinctions and proportion of functional diversity loss relative to other drivers of extinction, whereas control of hunting and collection and disturbance and accidental mortality would save fewer species but disproportionately boost functional richness. These findings show that conservation of avian diversity requires action partitioned across all drivers of decline, and highlights the importance of understanding and mitigating the ecological impacts of species extinctions predicted to occur even under optimistic levels of conservation action.

ecology↗

bioRxiv2022

Trait diversity metrics can perform well with highly incomplete datasets

O_LICharacterizing changes in trait diversity at large spatial scales provides insight into the impact of human activity on ecosystem structure and function. However, the approach is often based on trait datasets that are incomplete and unrepresentative, with uncertain impacts on trait diversity estimates. C_LIO_LITo address this knowledge gap, we simulated random and biased removal of data from a near complete avian trait dataset (9579 species) and assessed whether trait diversity metrics were robust to data incompleteness with and without using imputation to fill data gaps. Specifically, we compared two commonly used metrics each calculated with two methods: trait richness (calculated with convex hulls and trait probabilities densities) and trait divergence (calculated with distance-based Rao and trait probability densities). C_LIO_LIWithout imputation, estimates of global avian trait diversity (richness and divergence) were robust when 30-70% of species had missing data for four out of 11 continuous traits, depending on severity of bias and the method used. However, when missing traits were imputed based on present morphological trait data and phylogeny, trait diversity metrics consistently remained representative of the true value, even when 70% of species were missing data for four out of 11 traits and data were not missing at random (biased with respect to body mass). Trait probability densities and distance-based Rao were particularly robust to missingness and bias when combined with imputation, with convex hull-based trait richness being less reliable. C_LIO_LIExpanding global morphometric datasets to represent more taxa and traits, and to quantify intraspecific variation, remains a priority. In the meantime, our results show that widely used methods can successfully quantify large-scale trait diversity even when data are missing for two-thirds of species, so long as missing traits are estimated using imputation. C_LI

ecology↗

bioRxiv2022

Overconfidence undermines global wildlife abundance trends

In the face of rapid global change and an uncertain fate for biodiversity, it is vital to quantify trends in wild populations. These trends are typically estimated from abundance time series for suites of species across large geographic and temporal scales. Such data implicitly contain phylogenetic, spatial, and temporal structure which, if not properly accounted for, may obscure the true magnitude and direction of biodiversity change. Here, using a novel statistical framework to simultaneously account for all three of these structures, we show that the majority of current abundance trends estimates among 10 high-profile datasets, representing millions of abundance observations, are likely unreliable or incorrect. Our new approach suggests that previous models are too simplistic, incorrectly estimating global abundance trends and often dramatically underestimating uncertainty, an aspect that is critical when translating global assessments into policy outcomes. Further, our approach also results in substantial improvements in abundance forecasting accuracy. Whilst our results do not improve the outlook for biodiversity, our framework does allow us to make more robust estimates of global wildlife abundance trends, which is critical for developing policy to protect our biosphere.

ecology↗

bioRxiv2022

Past, present, and future of the Living Planet Index

As we enter the next phase of international policy commitments to halt biodiversity loss (e.g. Post-2020 Biodiversity Framework), biodiversity indicators will play an important role forming the robust basis upon which targeted, and time sensitive conservation actions are developed. Population trend indicators are perhaps the most powerful tool in biodiversity monitoring due to their responsiveness to changes over short timescales and their ability to aggregate species trends from global down to at a sub-national or even local scale. We consider how the project behind the foremost population level indicator - the Living Planet Index - has evolved over the last 25 years, its value to the field of biodiversity monitoring, and how its components have portrayed a compelling account of the changing status of global biodiversity through its application at policy, research and practice levels. We explore ways the project can develop to enhance our understanding of the state of biodiversity and share lessons learned to inform indicator development and mobilise action. O_TEXTBOXBox 1. The Living Planet Index Project The Living Planet Index project (the index, methodology, and database) and its secondary outputs (methods papers and R code, database and website, global index, and subset indices) have had wide-ranging applications within the fields of biodiversity monitoring and research, as well as across policy, education, and outreach. The Living Planet Index (LPI) is a biodiversity indicator which tracks trends in the relative abundance of wild vertebrate populations (where population is defined as to a single species in a defined location rather than the biological definition). Relative abundance captures how populations are changing over time on average in comparison to a reference point, or "baseline" (the LPI uses 1970). It is often described as analogous to a stock market index for species. The index is comprised of thousands of population time-series for vertebrate species from locations around the world; the trends from these populations are averaged to produce terrestrial, freshwater, and marine indices, which are further aggregated to a global LPI. The latest global LPI shows a decline of 68% between 1970 and 2016 globally 1. This is an average trend based on time-series data from 20,811 populations of 4,392 species of mammals, birds, reptiles, amphibians, and fish. The LPI database (LPD) can include population data for any species for which time-series population data could be found, regardless of threat status, or whether they show increasing or declining trends. These population time-series are sourced from scientific papers, online databases, government, and expert led published reports. They can be searched and downloaded from the project website (www.livingplanetindex.org). More technical information is available on the LPI stats website (http://stats.livingplanetindex.org/). C_TEXTBOX

ecology↗

bioRxiv2020

Body mass and latitude predict the presence of multiple stressors in global vertebrate populations

Multiple stressors are recognised as a key threat to biodiversity, but our understanding of what might predispose species to multiple stressors remains limited. Here we analyse a global dataset of over 7000 marine, freshwater, and terrestrial vertebrate populations, alongside species-specific trait data, to identify factors which influence the number of stressors a species is subjected to at the population level. We find that body mass and latitude can both influence the number of stressors a population is subjected to across ecosystems, with large-bodied species tending to be more threatened, except terrestrial amphibians which show the opposite trend. Latitudinal forecasts predict higher stressor numbers between 20{degrees}N and 40{degrees}N, and towards the poles. Global stressor distributions suggest a link between human population centres and stressor frequency generally impacting larger-bodied species. Latitude and body mass hence provide key predictive tools to identify which vertebrate populations are likely to be highly threatened, despite the strength of these trends differing between ecological system and taxonomic class.

ecology↗

bioRxiv2018

Weighted trait-abundance early warning signals better predict population collapse

Predicting population collapse in the face of unprecedented anthropogenic pressures is a key challenge in conservation. Abundance-based early warning signals have been suggested as a possible solution to this problem; however, they are known to be susceptible to the spatial and temporal subsampling ubiquitous to abundance estimates of wild population. Recent work has shown that composite early warning methods that take into account changes in fitness-related phenotypic traits - such as body size - alongside traditional abundance-based signals are better able to predict collapse, as trait dynamic estimates are less susceptible to sampling protocols. However, these previously developed composite early warning methods weighted the relative contribution of abundance and trait dynamics evenly. Here we present an extension to this work where the relative importance of different data types can be weighted in line with the quality of available data. Using data from a small-scale experimental system we demonstrate that weighted indicators can improve the accuracy of composite early warning signals by >60%. Our work shows that non-uniform weighting can increase the likelihood of correctly detecting a true positive early warning signal in wild populations, with direct relevance for conservation management.

ecology↗