Biology subjects

Clements, C. F.

Publications and source records attributed to Clements, C. F..

15 recordsLinked to original sources

bioRxiv2026

Vertebrates do not display consistent declines in abundance or stability with contemporary range or realized climate niche centrality

The abundant-centre (ACH) and stable-centre hypotheses (SCH) predict population abundance and stability decline with increasing distances from a species' range and multivariate climate niche centres. ACH and SCH are widely held assumptions underpinning many ecological and conservation applications, with range and climate centrality often assumed to reflect suitability. However, empirical support that abundance (distance-abundance) and stability (distance-variability) decline away from range and climate centres is mixed and hampered by radically different climate centrality assumptions. Here, we test distance-abundance relationships in >5000 vertebrate species and distance-variability relationships in >1000 vertebrate species across their realized geographic and climate niche spaces. We found no vertebrate taxa display consistent declines in abundance or stability with increasing distances from range centres or realized climate (niche) centres modelled from species' breeding ranges, native ranges or contemporary occurrences. Distance-abundance and distance-variability relationships were weak and inconsistent across taxa irrespective of centrality metric, modelling approach, climate variables, abundance metric or dataset. Contemporary vertebrate abundance patterns, therefore, are often decoupled from position in contemporary range and realized climate space, challenging a central assumption of core-periphery models. Broadly, our study indicates that frequently used range and climate centrality metrics may be weak predictors of vertebrate abundance patterns in contemporary landscapes and biological forecasting or climate change applications should be cautious of their unvalidated use.

ecology↗

bioRxiv2026

Time-lagged causal inference corroborates presumed drivers of North Atlantic seabird ecology

Disentangling causation from correlation is the foundation of the scientific method. Yet, growing evidence suggests that much observational ecology research has not correctly made this distinction due to inappropriate statistical modelling and unappreciated time-delays. Here, we apply time-lagged causal inference techniques to assess the drivers of seabird declines, using multi-decadal North Atlantic seabird data across the behaviour, mass, survival, reproduction and population size of two species ecology (Atlantic puffin, Fratercula arctica, and black-legged kittiwake, Rissa tridactyla). We demonstrate that both climate and anthropogenic activity can suppress breeding success and cause population declines. Moreover, population size is specifically impacted by delayed recruitment effects where both species decline after a lag corresponding to their estimated age of first reproduction. These North Atlantic seabirds are therefore at risk from future environmental and anthropogenic changes, as time-delays may result in populations already on an extinction trajectory prior to changes being detectable in their abundance.

ecology↗

bioRxiv2026

Pollination responses to artificial light at night may vary with lighting arrangement

Artificial light at night (ALAN) can alter the movement of nocturnal pollinators such as moths, with potential consequences for pollination services. However, most studies have focused on the presence or properties of lights, while the role of spatial lighting configuration remains poorly understood. We conducted a small-scale field experiment to test how different lighting configurations affect pollination success in moth-pollinated plants, using low-intensity LED garden lights. Potted phytometer plants of three moth-pollinated species were exposed to one of three treatments: no-light control, isolated LED point lights, or 25 m linear arrays of multiple LED lights. We quantified pollination success as both the probability of seed set and reproductive output through seed and capsule production. Experimental lighting affected plant reproductive success, with light arrays increasing seed-set probability, seed and seed capsule number relative to unlit controls, whereas point lights showed no clear effect. These results provide preliminary evidence that the spatial configuration of artificial lights may influence nocturnal pollination outcomes. Future work combining phytometer assays with direct tracking of moth movement is needed to assess whether light arrays facilitate or redirect the dispersal of nocturnal pollinators.

ecology↗

bioRxiv2025

The impact of artificial linear features on the pollination of plant communities

Linear features, such as road verges, hedgerows can function as corridors for species dispersal. Previous studies showed that these features could enhance movement of pollinators such as bees, increasing pollination success of bee pollinated plants. However, it remains unclear if these effects extend to communities of plants pollinated by other pollinators. We conducted a field experiment where we measured the pollination of phytometer plant communities linked by linear features. We constructed six 30m artificial linear features in both urban and rural meadows in the Southwest UK, and placed plant communities on either end of these linear features, comparing these to phytometers without linking linear features. Plant communities were assembled with seven species known to attract different groups of insect pollinators including bees, flies, and moths. We tested self-incompatibility of plant species and used their seed set to measure pollination. We found that linear features significantly improved the pollination success of three bee pollinated species, whilst the seed production of fly and moth pollinated species were either unaffected or less affected. Overall, it is likely that linear features benefit the pollination of some plant species more than others. Our results suggest that linear features play an important role in maintaining pollination for some plant species, highlighting the importance of evaluating community-level impacts of corridors on pollination. Given the importance of bees as pollinators, linear features can provide a practical strategy to conserve and improve pollination, but where species are fly or moth pollinated, alternative strategies may need to be considered.

ecology↗

bioRxiv2025

Halting predicted vertebrate declines requires tackling multiple drivers of biodiversity loss

Anthropogenic threats are reshaping Earths biodiversity at an unprecedented rate and scale1-3. Conservation policies often prioritise threats like habitat loss and exploitation based on their global prevalence. However, these assessments rarely quantify the impacts of individual or interacting threats, potential masking the true effects of the Anthropocene4-6. Here, we quantitatively analyse the trends of 3,129 vertebrate populations worldwide with documented exposure to specific and multiple threats. Populations impacted solely by habitat loss or exploitation, the most prevalent threats, do not show the fastest declines. Rather, populations exposed to disease, invasive species, pollution, and climate change decline more rapidly. However, habitat loss and exploitation - along with climate change - do act as additive interactive threats, amplifying population declines. Notably, these interactive threats contribute to population declines, more than temporal or spatial sources of variation. Finally, counterfactual scenarios show that to achieve global non-negative vertebrate population trends, we need to mitigate the effects of multiple threats. These findings underscore the urgency of addressing the compounding effects of multiple threats to halt biodiversity loss and suggest that the local-scale impacts of climate change may be more severe than previously recognized.

ecology↗

bioRxiv2023

Resilience metrics are robust across data qualities but sensitive to community size.

Modern biodiversity monitoring is generating increasingly multidimensional representations of wildlife populations and ecosystems. It is therefore appealing for conservation and environmental governance to combine that information into single measure of ecosystem or population health. Stability represents a desirable feature of ecosystems that supports this aim, measured through resistance, recovery, and variability. In deterministic mathematical systems, the Jacobian matrix is a common characteristic used to quantify resistance and resilience and whilst historically it has been challenging to estimate from empirical data, recent work has proposed a suite of metrics capable of reconstructing it for a real-world community using time series data. Here we assess the robustness of three Jacobian metrics and two variability estimating stability metrics to varying time series lengths and data qualities based on that seen in real-world wildlife time series. Using Lotka-Volterra equations, we generate short time series (to match global biodiversity datasets such as the Living Planet Index and BIOTIME) and introduce sampling error corruptions (to mimic varying search efforts) to validate metric performance in empirical data. The robustness stability metrics generally improved with time series length and search effort in the anticipated manner. However, number of species dramatically altered metric capability, with larger communities decreasing the reliability of stability metric trends. Overall, stability metrics behave predictably across realistic data corruptions. Generic stability estimation is therefore possible from abundance time series alone, and we suggest that, given the increasing availability of multivariate community data, focussing on Jacobian estimates is a plausible ecosystem condition indicator.

ecology↗

bioRxiv2023

Early warning signals are hampered by a lack of critical transitions in empirical lake data

Quantifying the potential for abrupt non-linear changes in ecological communities is a key managerial goal, leading to a significant body of research aimed at identifying indicators of approaching regime shifts. Most of this work has built on the theory of bifurcations, with the assumption that critical transitions are a common feature of complex ecological systems. This has led to the development of a suite of often inaccurate early warning signals (EWSs), with more recent techniques seeking to overcome their limitations by analysing multivariate time series or applying machine learning. However, it remains unclear whether regime shifts and/or critical transitions are common occurrences in natural systems, and - if they are present - whether classic and second-generation EWS methods predict rapid community change. Here, using multitrophic data on nine lakes from around the world, we both identify the type of transition a lake is exhibiting, and the reliability of classic and second generation EWSs methods to predict whole ecosystem change. We find few instances of critical transitions in our lake dataset, with different trophic levels often expressing different forms of abrupt change. The ability to predict this change is highly technique dependant, with multivariate EWSs generally classifying correctly, classical rolling window univariate EWSs performing not better than chance, and recently developed machine learning techniques performing poorly. Our results suggest that predictive ecology should start to move away from the concept of critical transitions and develop methods suitable for predicting change in the absence of the strict bounds of bifurcation theory.

ecology↗

bioRxiv2022

Restoring local climate refugia to enhance the capacity for dispersal-limited species to track climate change

Climate refugia are areas where species can persist through climate change with little to no movement. Among the factors associated with climate refugia are high spatial heterogeneity, such that there is only a short distance between current and future optimal climates, as well as biotic or abiotic environmental factors which buffer against variability in time. However, climate refugia may be declining due to anthropogenic homogenization of environments and degradation of environmental buffers. To quantify the potential for restoration of refugia-like environmental conditions to increase population persistence under climate change, we simulated a populations capacity to track increasing temperatures over time given different levels of spatial and temporal variability in temperature. To determine how species traits affected the efficacy of restoring heterogeneity, we explored an array of values for species dispersal ability, thermal tolerance, and fecundity. We found that species were more likely to persist in environments with higher local heterogeneity and lower environmental stochasticity. When simulating a management action that increased the local heterogeneity of a previously homogenized environment, species were more likely to persist through climate change, and population sizes were generally higher, but there was little effect with mild temperature change. The benefits of heterogeneity restoration were greatest for species with limited dispersal ability. In contrast, species with longer dispersal but lower fecundity were more likely to benefit from a reduction in environmental stochasticity than an increase in spatial heterogeneity. Our results suggest that restoring environments to refugia-like conditions could promote species persistence under climate change in addition to conservation strategies such as assisted migration, corridors, and increased protection.

ecology↗

bioRxiv2022

Planktonic functional diversity changes in synchrony with lake ecosystem state

Managing ecosystems to effectively preserve function and services requires reliable tools that can infer changes in the stability and dynamics of a system. Conceptually, functional diversity (FD) appears a viable monitoring metric due to its mechanistic influence on ecological processes, but it is unclear whether changes in FD occur prior to state responses or vice versa. We examine the lagged relationship between planktonic FD and abundance-based metrics of system state (e.g. biomass) across five highly monitored lake communities using both correlation and non-linear causality approaches. Overall, phytoplankton and zooplankton FD display synchrony with lake state but each lake is idiosyncratic in the strength of relationship. It is therefore unlikely that changes in plankton FD are identifiable before changes in more easily collected abundance metrics. This suggests that FD is unlikely to be a viable early indicator, but has value as an alternative state measure if considered at the lake level. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/495076v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@7c688org.highwire.dtl.DTLVardef@7b170borg.highwire.dtl.DTLVardef@125e6e9org.highwire.dtl.DTLVardef@15af9bf_HPS_FORMAT_FIGEXP M_FIG C_FIG Data accessibility statementLake Kinneret and Lake Kasumigaura data are available on request, with all other data publicly available and referenced throughout. All code for analysis is available in the Zenodo record (to be released) and the associated GitHub repository (https://github.com/duncanobrien/plankton-FD).

ecology↗

bioRxiv2022

Community structure determines the predictability of population collapse

O_LIEarly warning signals (EWS) are phenomenological tools that have been proposed as predictors of the collapse of biological systems. Whilst a growing body of work has shown the utility of EWS based on either statistic derived from abundance data or shifts in phenotypic traits such as body size, so far this work has largely focused on single species populations. C_LIO_LIHowever, in order to predict reliably the future state of ecological systems which inherently could consist of multiple species, understanding how reliable such signals are in a community context is critical. C_LIO_LIHere, reconciling quantitative genetics and Lotka-Volterra equations which allow us to track both abundance and mean traits, we simulate the collapse of populations embedded in mutualistic and multi-trophic predator-prey communities. Using these simulations and warning signals derived from both population- and community-level data, we show that the utility of abundance-based EWS as well as metrics derived from stability-landscape theory (e.g., width and depth of the basin of attraction) are fundamentally linked, and thus the depth and width of the stability landscape could be used to identify which species should exhibit the strongest EWS of collapse. C_LIO_LIThe probability a species displays both trait and abundance based EWS is dependent on its position in a community, with some species able to act as indicators species. In addition, our results also demonstrate that in general trait-based EWS appear less reliable in comparison to abundance-based EWS in forecasting species collapses in our simulated communities. Furthermore, community-level abundance-based EWS were fairly reliable in comparison to their species-level counterparts in forecasting species level collapses. C_LIO_LIOur study suggests a holistic framework that combines abundance-based EWS and metrics derived from stability-landscape theory that may help in forecasting species loss in a community context. C_LI

ecology↗

bioRxiv2022

Body mass and latitude as global predictors of vertebrate populations exposure to multiple threats

The interactive effects of multiple threats are one of the main causes of biodiversity loss, yet our understanding of what predisposes species to be impacted by multiple threats remains limited. Here we analyse a global dataset of over 7000 marine, freshwater, and terrestrial vertebrate populations, alongside trait, threat and geographical data, to identify the factors influencing the number of threats a species is subjected to at the population level. Out of a suite of predictors tested, we find that body mass and latitude both are broadly available for vertebrate species, and influence the number of threats a population is subjected to. Larger bodied species and those nearer the equator are typically affected by a higher number of threats. However, whilst this pattern broadly holds across ecosystems for most taxa, amphibians and reptiles show opposing trends. We suggest that latitude and body mass should be considered as key predictors to identify which vertebrate populations are likely to be impacted by multiple threats. These general predictors can help to better understand the impacts of the Anthropocene on global vertebrate biodiversity and design effective conservation policies.

ecology↗

bioRxiv2021

Machine learning methods trained on simple models can predict critical transitions in complex natural systems

1. Sudden transitions from one stable state to a contrasting state occur in complex systems ranging from the collapse of ecological populations to climatic change, with much recent work seeking to develop methods to predict these unexpected transitions from signals in time series data. However, previously developed methods vary widely in their reliability, and fail to classify whether an approaching collapse might be catastrophic (and hard to reverse) or non-catastrophic (easier to reverse) with significant implications for how such systems are managed. 2. Here we develop a novel detection method, using simulated outcomes from a range of simple mathematical models with varying nonlinearity to train a deep neural network to detect critical transitions - the Early Warning Signal Network (EWSNet). 3. We demonstrate that this neural network (EWSNet), trained on simulated data with minimal assumptions about the underlying structure of the system, can predict with high reliability observed real-world transitions in ecological and climatological data. Importantly, our model appears to capture latent properties in time series missed by previous warning signals approaches, allowing us to not only detect if a transition is approaching but critically whether the collapse will be catastrophic or non-catastrophic. 4. The EWSNet can flag a critical transition with unprecedented accuracy, overcoming some of the major limitations of traditional methods based on phenomena such as Critical Slowing Down. These novel properties mean EWSNet has the potential to serve as a universal indicator of transitions across a broad spectrum of complex systems, without requiring information on the structure of the system being monitored. Our work highlights the practicality of deep learning for addressing further questions pertaining to ecosystem collapse and have much broader management implications.

ecology↗

bioRxiv2019

Effect of time-series length and resolution on abundance- and trait-based early warning signals of population declines

Natural populations are increasingly threatened with collapse at the hands of anthropogenic effects. Predicting population collapse with the help of generic early warning signals (EWS) may provide a prospective tool for identifying species or populations at highest risk. However, pattern-to-process methods such as EWS have a multitude of challenges to overcome to be useful, including the low signal to noise ratio of ecological systems and the need for high quality time-series data. The inclusion of trait dynamics with EWS has been proposed as a more robust tool to predict population collapse. However, the length and resolution of available time series are highly variable from one system to another, especially when generation time is considered. As yet it remains unknown how this variability with regards to generation time will alter the efficacy of EWS. Here we take both a simulation- and experimental-based approach to assess the impacts of relative time-series length and resolution on the forecasting ability of EWS. We show that EWS performance decreases with decreasing length and resolution. Our simulations suggest a relative time-series length between ten and five generations and a resolution of half a generation are the minimum requirements for accurate forecasting by abundance-based EWS. However, when trait information is included alongside abundance-based EWS, we find positive signals at lengths and resolutions half of what was required without them. We suggest that, in systems where specific traits are known to affect demography, trait data should be monitored and included alongside abundance data to improve forecasting reliability.

ecology↗

bioRxiv2018

When do shifts in trait dynamics precede population declines?

Predicting population responses to environmental change is an on-going challenge in ecology. Studies investigating the links between fitness-related phenotypic traits and demography have shown that trait dynamic responses to environmental change can sometimes precede population dynamic responses, and thus, can be used as an early warning signal. However, it is still unknown under which ecological and evolutionary circumstances, shifts in fitness-related traits can precede population responses to environmental perturbation. Here, we take a trait-based demographic approach and investigate both trait and population dynamics in a density-regulated population in response to a gradual change in the environment. We explore the ecological and evolutionary constraints under which shifts in a fitness-related trait precedes a decline in population size. We show both analytically and with experimental data that under medium-to-slow rate of environmental change, shifts in trait value can precede population decline. We further show the positive influence of environmental predictability, average reproductive rate, plasticity, and genetic variation on shifts in trait dynamics preceding potential population declines. These results still hold under non-constant genetic variation and environmental stochasticity. Our study highlights ecological and evolutionary circumstances under which a fitness-related trait can be used as an early warning signal of an impending population decline.

ecology↗

bioRxiv2018

Eco-evolutionary processes underlying early warning signals of population declines

O_LIEnvironmental change can impact the stability of populations and can cause rapid declines in abundance. Abundance-based warning signals have been proposed to predict such declines, but these have been shown to have limited success, leading to the development of warning signals based on the distribution of fitness-related traits such as body size. C_LIO_LIThe dynamics of such traits in response to external environmental perturbations are controlled by a range of underlying factors such as reproductive rate, genetic variation, and plasticity. However, it remains unknown how such ecological and evolutionary factors affect the stability landscape of populations and the detectability of abundance and trait-based warning signals of population decline. C_LIO_LIHere, we apply a trait-based demographic approach and investigate both trait and population dynamics in response to gradual changes in the environment. We explore a range of ecological and evolutionary constraints under which the stability of a population may be affected. C_LIO_LIWe show both analytically and with model-based simulations that strength of abundance-based early warning signals is significantly affected by ecological and evolutionary factors. C_LIO_LIFinally, we show that a unified approach, combining trait- and abundance-based information, significantly improves our ability to predict population declines. Our study suggests that the inclusion of trait dynamic information alongside generic warning signals should provide more accurate forecasts of the future state of biological systems. C_LI

ecology↗