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Contemporary rates of biodiversity decline emphasize the need for reliable ecological forecasting, but current methods vary in their ability to predict the declines of real-world populations. Acknowledging that stressor effects start at the individual level, and that it is the sum of these individual-level effects that drives populations to collapse, shifts the focus of predictive ecology away from using predominantly abundance data. Doing so opens new opportunities to develop predictive frameworks that utilize increasingly available multi-dimensional data, which have previously been overlooked for ecological forecasting. Here, we propose that stressed populations will exhibit a predictable sequence of observable changes through time: changes in individuals’ behaviour will occur as the first sign of increasing stress, followed by changes in fitness-related morphological traits, shifts in the dynamics (for example, birth rates) of populations and finally abundance declines. We discuss how monitoring the sequential appearance of these signals may allow us to discern whether a population is increasingly at risk of collapse, or is adapting in the face of environmental change, providing a conceptual framework to develop new forecasting methods that combine multi-dimensional (for example, behaviour, morphology, life history and abundance) 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.

Context Animal-mediated pollination is a key factor that determines the reproductive success of the most flowering plants; this process however can be disrupted by environmental degradation, with habitat fragmentation highlighted as a key driver of pollinator declines. Despite habitat fragmentation being one of the most pervasive anthropogenic stressors worldwide, we still have rather limited empirical evidence on its effects on pollination, especially for early spring pollination syndromes. Objectives We experimentally study the effect of patch area and isolation on the pollination of English Bluebell (Hyacinthoides non-scripta), a species largely pollinated in spring by queen bumblebees. Methods In a fragmented landscape in Bristol, United Kingdom, we selected 51 woodland patches which vary in both size and distance from each other, and placed 153 bluebell plants in those selected patches for c.4 weeks to measure pollination. Results Measuring pollination through the number of seeds produced and seed capsules formed, we show that while patch area had no effect, the main determinate of overall reproductive success of plants was patch isolation which negatively correlated with both seed number and capsules. Conclusion Our results highlight the importance of connectivity in maintaining pollination services in fragmented landscapes.

Biodiversity is declining at an unprecedented rate, highlighting the urgent requirement for well-designed protected areas. Design tactics previously proposed to promote biodiversity include enhancing the number, connectivity, and heterogeneity of reserve patches. However, how the importance of these features changes depending on what the conservation objective is remains poorly understood. Here we use experimental landscapes containing ciliate protozoa to investigate how the number and heterogeneity in size of habitat patches, rates of dispersal between neighbouring patches, and mortality risk of dispersal across the non-habitat ‘matrix’ interact to affect a number of diversity measures. We show that increasing the number of patches significantly increases γ diversity and reduces the overall number of extinctions, whilst landscapes with heterogeneous patch sizes have significantly higher γ diversity than those with homogeneous patch sizes. Furthermore, the responses of predators depended on their feeding specialism, with generalist predator presence being highest in a single large patch, whilst specialist predator presence was highest in several-small patches with matrix dispersal. Our evidence emphasises the importance of considering multiple diversity measures to disentangle community responses to patch configuration.

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components—for example, individual behaviours and traits, and species abundance and distribution—is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

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.

1. Temperature is a fundamental driver of species' vital rates and thus coexistence, extinctions and community composition. While temperature is neither static in space nor in time, little work has incorporated spatiotemporal dynamics into community-level investigations of thermal variation.2. We conducted a microcosm experiment using ciliate protozoa to test the effects of temperatures fluctuating synchronously or asynchronously on communities in two-patch landscapes connected by short or long corridors.3. We monitored the abundance of each species for 4 weeks—equivalent to ~28 generations—measuring the effects of four temperature regimes and two corridor lengths on community diversity and composition.4. While corridor length significantly altered the trajectory of diversity change in the communities, this did not result in different community structures at the end of the experiment. The type of thermal variation significantly affected both the temporal dynamics of diversity change and final community composition, with synchronous fluctuation causing deterministic extinctions that were consistent across replicates and spatial variation causing the greatest diversity declines.5. Our results suggest that the presence and type of thermal variation can play an important role in structuring ecological communities, especially when it interacts with dispersal between habitat patches.

Early 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.However, 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.Here, 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.The 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.Our 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.

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 as a sensitive and viable monitoring metric stemming from suggestions that FD is a universally important measure of biodiversity and has a mechanistic influence on ecological processes. It is however unclear whether changes in FD consistently occur prior to state responses or vice versa, with no current work on the temporal relationship between FD and state to support a transition towards trait-based indicators. There is consequently a knowledge gap regarding when functioning changes relative to biodiversity change and where FD change falls in that sequence. We therefore 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 cutting edge non-linear empirical dynamic modelling 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. These results highlight the power of empirical dynamic modelling in disentangling time lagged relationships in complex multivariate ecosystems, but suggest that FD cannot be generically viable as an early indicator. Individual lakes therefore require consideration of their specific context and any interpretation of FD across systems requires caution. However, FD still retains value as an alternative state measure or a trait representation of biodiversity when considered at the system level.

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 population’s 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.

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 AbstractData accessibility statement:Lake 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).

Mutual reinforcement between abiotic and biotic factors can drive small populations into a catastrophic downward spiral to extinction—a process known as the “extinction vortex.” However, empirical studies investigating extinction dynamics in relation to species' traits have been lacking.We assembled a database of 35 vertebrate populations monitored to extirpation over a period of at least ten years, represented by 32 different species, including 25 birds, five mammals, and two reptiles. We supplemented these population time series with species‐specific mean adult body size to investigate whether this key intrinsic trait affects the dynamics of populations declining toward extinction.We performed three analyses to quantify the effects of adult body size on three characteristics of population dynamics: time to extinction, population growth rate, and residual variability in population growth rate.Our results provide support for the existence of extinction vortex dynamics in extirpated populations. We show that populations typically decline nonlinearly to extinction, while both the rate of population decline and variability in population growth rate increase as extinction is approached. Our results also suggest that smaller‐bodied species are particularly prone to the extinction vortex, with larger increases in rates of population decline and population growth rate variability when compared to larger‐bodied species.Our results reaffirm and extend our understanding of extinction dynamics in real‐life extirpated populations. In particular, we suggest that smaller‐bodied species may be at greater risk of rapid collapse to extinction than larger‐bodied species, and thus, management of smaller‐bodied species should focus on maintaining higher population abundances as a priority., Our results reaffirm and extend our understanding of extinction dynamics in real‐life extirpated populations. In particular, we suggest that smaller‐bodied species may be at greater risk of rapid collapse to extinction than larger‐bodied species, and thus management of smaller‐bodied species should focus on maintaining higher population abundances as a priority.

Loss of resilience in population numbers in response to environmental perturbations may be predicted with statistical metrics called early warning signals (EWS) that are derived from abundance time series. These signals, however, have been shown to have limited success, leading to the development of trait‐based EWS that are based on information collected from phenotypic traits such as body size. Experimental work assessing the efficacy of EWS under varying ecological and environmental factors are rare. In addition, disentangling how such warning signals are affected under varying ecological and environmental factors is key to their application in biological conservation. Here, we experimentally test how different rates of environmental forcing (i.e. warming) and varying ecological factors (i.e. habitat quality and phenotypic diversity) affected population stability and predictive power of early warning signals of population collapse. We analyzed population density and body size time series data from three phenotypically different populations of a protozoan ciliate Askenasia volvox in two levels of habitat quality subjected to three different treatments of warming (i.e. no warming, fast warming and slow warming). We then evaluated how well abundance‐ and trait‐based EWS predicted population collapses under different levels of phenotypic diversity, habitat quality and warming treatments. Our results suggest that habitat quality and warming treatments had more profound effects than phenotypic diversity had on both population stability and on the performance of abundance‐based signals of population collapse. In addition, trait‐based EWS generally performed well, were reliable and more robust in forecasting population collapse than abundance‐based EWS, regardless of variation in environmental and ecological factors. Our study points towards the development of a predictive framework that includes information from phenotypic traits such as body size as an indicator of loss of resilience of ecological systems in response to environmental perturbations.

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.

Maintaining the resilience of natural populations, their ability to resist and recover from disturbance, is crucial to prevent biodiversity loss. However, the lack of appropriate data and quantitative tools has hampered our understanding of the factors determining resilience on a global scale. Here, we quantified the temporal trends of two key components of resilience—resistance and recovery—in >2000 population time-series of >1000 vertebrate species globally. We show that the number of threats to which a population is exposed is the main driver of resilience decline in vertebrate populations. Such declines are driven by a non-uniform loss of different components of resilience (i.e. resistance and recovery). Increased anthropogenic threats accelerating resilience loss through a decline in the recovery ability—but not resistance—of vertebrate populations. These findings suggest we may be underestimating the impacts of global change, highlighting the need to account for the multiple components of resilience in global biodiversity assessments.

Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the initial emergence of disease outbreaks, offering hope that policy makers can make predictive rather than reactive management decisions. Here, using daily COVID-19 case data in combination with a novel, sequential analysis, we show that composite EWSs consisting of variance, autocorrelation, and return rate not only pre-empt the initial emergence of COVID-19 in the UK by 14 to 29 days, but also the following wave six months later. We also predict there is a high likelihood of a third wave as of the data available on 9th June 2021. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policy makers to improve the accuracy of time critical decisions based solely upon surveillance data.

The GW4 Alliance brings together the universities of Bath, Bristol, Cardiff and Exeter. We are the only research alliance collaborating across England and Wales, benefiting from the context of devolved administration perspectives and giving us a unique position in applying world-leading water research to policy implementation across different industry, governance and healthcare systems. Our GW4 Chair will host a 75-minute panel on research which makes use of the geographical resource our Alliance has in abundance: water. With three brief, engaging, presentations from each panelist, the session will continue to consider how regionally based research can drive international progress. The panel will reflect on their direct experience, inviting audience questions throughout.

Environmental 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.The 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.Here, 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.We show both analytically and with model-based simulations that strength of abundance-based early warning signals is significantly affected by ecological and evolutionary factors.Finally, 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.

Corridors are expected to increase species dispersal in fragmented habitats. However, it remains unclear how the quality of corridors influences the dispersal process, and how it interacts with corridor length and width. Here we investigate these factors using a small-scale laboratory system where we track the dispersal of the model organism Collembola Folsomia candida. Using this system, we study the effects of corridor length, width, and quality on the probability of dispersal, net movement, body size of dispersers, and the rate of change in population size after colonization. We show that corridor quality positively affected dispersal probability, net movement, and the rate of change in population size in colonised patches. Moreover, corridor quality significantly affected the size of dispersers, with only larger individuals dispersing through poor quality corridors. The length and width of corridors affected both the rate at which populations increased in colonised patches and the net number of individuals which dispersed, suggesting that these physical properties may be important in maintaining the flow of individuals in space. Our results thus suggest that corridor quality can have an important role in determining not only the probability of dispersal occurs but also the phenotypes of the individuals which disperse, with concomitant effects on the net movement of individuals and the rate of change in population size in the colonised patches. Supplementary Information The online version contains supplementary material available at 10.1007/s00442-020-04834-2.

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°N and 40°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.

Understanding the dynamics of small populations is critical to conserve those species at most risk. Previous work has identified demographic and environmental factors that can mutually reinforce one-another to drive populations rapidly to extinction – a process known as the ‘extinction vortex.’ However, studies investigating robustness to the extinction vortex in relation to life history and ecological traits have been lacking. Here, we assemble a database of 55 vertebrate populations monitored to extirpation and perform three analyses to investigate whether a key fitness-related phenotypic trait – body size – influences the rate at which populations succumb to the extinction vortex. We find evidence that populations of smaller-bodied species deteriorate at a faster rate, suggesting that intrinsic biological traits can alter the susceptibility of species to the extinction vortex, and may serve as a useful feature for prioritizing which populations to invest conservation effort in.

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 time-series length. However, there was no evident decrease in EWS performance as resolution decreased. Our simulations suggest a relative time series length between 10 and five generations as a minimum requirement for accurate forecasting by abundance-based EWS. However, when trait information is included alongside abundance-based EWS, we find positive signals at lengths one-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.

Designing protected area configurations to maximise biodiversity is a critical conservation goal. The configuration of protected areas can significantly impact the richness and identity of the species found there; one large patch supports larger populations but can facilitate competitive exclusion. Conversely, many small habitats spreads risk but may exclude predators that typically require large home ranges. Identifying how best to design protected areas is further complicated by monitoring programs failing to detect species. Here we test the consequences of different protected area configurations using multi-trophic level experimental microcosms. We demonstrate that for a given total size, many small patches generate higher species richness, are more likely to contain predators, and have fewer extinctions compared to single large patches. However, the relationship between the size, number of patches, and species richness was greatly affected by insufficient monitoring, and could lead to incorrect conservation decisions, especially for higher trophic levels.

Early warning signals (EWSs) offer the hope that patterns observed in data can predict the future states of ecological systems. While a large body of research identifies such signals prior to the collapse of populations, the prediction that such signals should also be present before a system’s recovery has thus far been overlooked. We assess whether EWSs are present prior to the recovery of overexploited marine systems using a trait-based ecological model and analysis of real-world fisheries data. We show that both abundance and trait-based signals are independently detectable prior to the recovery of stocks, but that combining these two signals provides the best predictions of recovery. This work suggests that the efficacy of conservation interventions aimed at restoring systems which have collapsed may be predicted prior to the recovery of the system, with direct relevance for conservation planning and policy., While several studies have documented early warning signals of population collapse, the use of such signals as indicators of population recovery has not been investigated. Here the authors use models and empirical fisheries data to show that there are statistical indicators preceding recovery of cod populations.

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.

Aim Given its catastrophic consequences, the extinction of apex predators has long been of interest to modern ecology. Despite major declines, no present-day species of marine apex predator has yet become extinct. Because of their vulnerability, understanding the mechanisms leading to their extinction in the past could provide insight into the natural factors that interact with human threats to drive their loss. We studied the geographical distribution patterns of the extinct macro-predatory shark Carcharocles megalodon in order to elucidate its pathway to extinction. Location World-wide from the Miocene to the Pliocene (c. 23–2.6 Ma). Methods A meta-analysis of C. megalodon occurrence records was performed using the Paleobiology Database as a platform. The data were binned into geological time slices, and the circular home range around each data point was mapped in reconstructions made in GPlates. We then quantitatively assessed the species' geographical range and global abundance over time, and the relationship between distribution and climate. Results The pathway to extinction of C. megalodon probably started in the late Miocene with a decrease in its global abundance. This decrease was then followed by a decline in its geographical range during the Pliocene. Although the extinction of C. megalodon has been attributed to climate change, we found no evidence of direct effects of global temperature. Instead, we found that the collapse in geographical distribution coincided mainly with a drop in the diversity of filter-feeding whales and the appearance of new competitors (large predatory whales and the great white shark). Main conclusions This research represents the first study of the distributional trends of an extinct, cosmopolitan apex predator in deep-time. Our results suggest that biotic factors, and not direct temperature limitations, were probably the primary drivers of the extinction of the largest marine apex predators that ever lived.

Parasitic species, which depend directly on host species for their survival, represent a major regulatory force in ecosystems and a significant component of Earth's biodiversity. Yet the negative impacts of parasites observed at the host level have motivated a conservation paradigm of eradication, moving us farther from attainment of taxonomically unbiased conservation goals. Despite a growing body of literature highlighting the importance of parasite-inclusive conservation, most parasite species remain understudied, underfunded, and underappreciated. We argue the protection of parasitic biodiversity requires a paradigm shift in the perception and valuation of their role as consumer species, similar to that of apex predators in the mid-20th century. Beyond recognizing parasites as vital trophic regulators, existing tools available to conservation practitioners should explicitly account for the unique threats facing dependent species. We built upon concepts from epidemiology and economics (e.g., host-density threshold and cost-benefit analysis) to devise novel metrics of margin of error and minimum investment for parasite conservation. We define margin of error as the risk of accidental host extinction from misestimating equilibrium population sizes and predicted oscillations, while minimum investment represents the cost associated with conserving the additional hosts required to maintain viable parasite populations. This framework will aid in the identification of readily conserved parasites that present minimal health risks. To establish parasite conservation, we propose an extension of population viability analysis for host-parasite assemblages to assess extinction risk. In the direst cases, ex situ breeding programs for parasites should be evaluated to maximize success without undermining host protection. Though parasitic species pose a considerable conservation challenge, adaptations to conservation tools will help protect parasite biodiversity in the face of an uncertain environmental future.

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.

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.

Trophic cascades are indirect positive effects of predators on resources via control of intermediate consumers. Larger-bodied predators appear to induce stronger trophic cascades (a greater rebound of resource density toward carrying capacity), but how this happens is unknown because we lack a clear depiction of how the strength of trophic cascades is determined. Using consumer resource models, we first show that the strength of a trophic cascade has an upper limit set by the interaction strength between the basal trophic group and its consumer and that this limit is approached as the interaction strength between the consumer and its predator increases. We then express the strength of a trophic cascade explicitly in terms of predator body size and use two independent parameter sets to calculate how the strength of a trophic cascade depends on predator size. Both parameter sets predict a positive effect of predator size on the strength of a trophic cascade, driven mostly by the body size dependence of the interaction strength between the first two trophic levels. Our results support previous empirical findings and suggest that the loss of larger predators will have greater consequences on trophic control and biomass structure in food webs than the loss of smaller predators.

In the face of global biodiversity declines, predicting the fate of biological systems is a key goal in ecology. One popular approach is the search for early warning signals (EWSs) based on alternative stable states theory. In this review, we cover the theory behind nonlinearity in dynamic systems and techniques to detect the loss of resilience that can indicate state transitions. We describe the research done on generic abundance-based signals of instability that are derived from the phenomenon of critical slowing down, which represent the genesis of EWSs research. We highlight some of the issues facing the detection of such signals in biological systems - which are inherently complex and show low signal-to-noise ratios. We then document research on alternative signals of instability, including measuring shifts in spatial autocorrelation and trait dynamics, and discuss potential future directions for EWSs research based on detailed demographic and phenotypic data. We set EWSs research in the greater field of predictive ecology and weigh up the costs and benefits of simplicity vs. complexity in predictive models, and how the available data should steer the development of future methods. Finally, we identify some key unanswered questions that, if solved, could improve the applicability of these methods.

Carlson, Colin J. et al., Climate change is a well-documented driver of both wildlife extinction and disease emergence, but the negative impacts of climate change on parasite diversity are undocumented. We compiled the most comprehensive spatially explicit data set available for parasites, projected range shifts in a changing climate, and estimated extinction rates for eight major parasite clades. On the basis of 53,133 occurrences capturing the geographic ranges of 457 parasite species, conservative model projections suggest that 5 to 10% of these species are committed to extinction by 2070 from climate-driven habitat loss alone. We find no evidence that parasites with zoonotic potential have a significantly higher potential to gain range in a changing climate, but we do find that ectoparasites (especially ticks) fare disproportionately worse than endoparasites. Accounting for host-driven coextinctions, models predict that up to 30% of parasitic worms are committed to extinction, driven by a combination of direct and indirect pressures. Despite high local extinction rates, parasite richness could still increase by an order of magnitude in some places, because species successfully tracking climate change invade temperate ecosystems and replace native species with unpredictable ecological consequences., This project was funded in part by the UC Berkeley Department of Environmental Science, Policy, and Management, by the A. Starker Leopold Chair held by W.M.G., and by project CGL2015-69650-P and Ramon y Cajal research contract (RYC-2009-03967) to R.J. The creation of the feather mite database was supported by a Natural Sciences and Engineering Research Council of Canada Discovery Grant to H.C.P., We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).

Anthropogenic pressures on the global biome are causing widespread species declines and extinctions. Assessing the extinction risk faced by individual species is a critical first step in combating this trend. However, we lack high quality demographic data to do so for the vast majority of plant and animal species. We present an efficient modeling approach to estimate extinction risk based on a statistical framework from the mark-recapture literature. We assessed the model's performance using a combination of simulated data, results from a protist microcosm experiment, and data from a long-term, large-scale habitat fragmentation experiment in southeastern Australia. Simulation experiments showed the model is robust to missing data as well as biological processes not included explicitly in the model's assumptions. Fitting the model to data from the protist experiment yielded accurate predictions of the regional extinction dynamics observed in the system, even with a relatively low level of replication. Finally, the model was able to accurately predict the observed dynamics in the habitat fragmentation experiment. The model provides a robust and accurate method to evaluate a species' extinction risk. Since it only requires presence/absence data, applies to a wide range of survey designs, and allows for observational uncertainty and missing data, it can be applied to many datasets that existing models cannot accommodate. For these reasons, the model should be useful in conservation settings.

Individuals in a population vary in their growth due to hidden and observed factors such as age, genetics, environment, disease, and carryover effects from past environments. Because size affects fitness, growth trajectories scale up to affect population dynamics. However, it can be difficult to estimate growth in data from wild populations with missing observations and observation error. Previous work has shown that linear mixed models (LMMs) underestimate hidden individual heterogeneity when more than 25% of repeated measures are missing. Here we demonstrate a flexible and robust way to model growth trajectories. We show that state-space models (SSMs), fit using R package growmod, are far less biased than LMMs when fit to simulated data sets with missing repeated measures and observation error. This method is much faster than Markov chain Monte Carlo methods, allowing more models to be tested in a shorter time. For the scenarios we simulated, SSMs gave estimates with little bias when up to 87.5% of repeated measures were missing. We use this method to quantify growth of Soay sheep, using data from a long-term mark-recapture study, and demonstrate that growth decreased with age, population density, weather conditions, and when individuals are reproductive. The method improves our ability to quantify how growth varies among individuals in response to their attributes and the environments they experience, with particular relevance for wild populations.

Despite the number of virulent pathogens that are projected to benefit from global change and to spread in the next century, we suggest that a combination of coextinction risk and climate sensitivity could make parasites at least as extinction prone as any other trophic group. However, the existing interdisciplinary toolbox for identifying species threatened by climate change is inadequate or inappropriate when considering parasites as conservation targets. A functional trait approach can be used to connect parasites' ecological role to their risk of disappearance, but this is complicated by the taxonomic and functional diversity of many parasite clades. Here, we propose biological traits that may render parasite species particularly vulnerable to extinction (including high host specificity, complex life cycles and narrow climatic tolerance), and identify critical gaps in our knowledge of parasite biology and ecology. By doing so, we provide criteria to identify vulnerable parasite species and triage parasite conservation efforts.

Correctly classifying a species as extinct or extant is of critical importance if current rates of biodiversity loss are to be accurately quantified. Observing an extinction event is rare, so in many cases extinction status is inferred using methods based on the analysis of records of historic sighting events. The accuracy of such methods is difficult to test. However, results of recent experiments with microcosm communities suggest that the rate at which a population declines to extinction, potentially driven by varying environmental conditions, may alter one's ability accurately to infer extinction status. We tested how the rate of population decline, driven by historic environmental change, alters the accuracy of 6 commonly applied sighting-based methods used to infer extinction. We used data from small-scale experimental communities and recorded wild population extirpations. We assessed how accuracy of the different methods was affected by rate of population decline, search effort, and number of sighting events recorded. Rate of population decline and historic population size of the species affected the accuracy of inferred extinction dates; however, faster declines produced more accurate inferred dates of extinction, but only when population sizes were higher. Optimal linear estimation (OLE) offered the most reliable and robust estimates, though no single method performed best in all situations, and it may be appropriate to use a different method if information regarding historic search efforts is available. OLE provided the most accurate estimates of extinction when the number of sighting events used was >10, and future use of this method should take this into account. Data from experimental populations provide added insight into testing techniques to discern wild extirpation events. Care should be taken designing such experiments so that they mirror closely the abundance dynamics of populations affected by real-world extirpation events.

Changing temperature can substantially shift ecological communities by altering the strength and stability of trophic interactions. Because many ecological rates are constrained by temperature, new approaches are required to understand how simultaneous changes in multiple rates alter the relative performance of species and their trophic interactions. We develop an energetic approach to identify the relationship between biomass fluxes and standing biomass across trophic levels. Our approach links ecological rates and trophic dynamics to measure temperature-dependent changes to the strength of trophic interactions and determine how these changes alter food web stability. It accomplishes this by using biomass as a common energetic currency and isolating three temperature-dependent processes that are common to all consumer-resource interactions: biomass accumulation of the resource, resource consumption and consumer mortality. Using this framework, we clarify when and how temperature alters consumer to resource biomass ratios, equilibrium resilience, consumer variability, extinction risk and transient vs. equilibrium dynamics. Finally, we characterise key asymmetries in species responses to temperature that produce these distinct dynamic behaviours and identify when they are likely to emerge. Overall, our framework provides a mechanistic and more unified understanding of the temperature dependence of trophic dynamics in terms of ecological rates, biomass ratios and stability. © 2014 John Wiley & Sons Ltd/CNRS.

Biodiversity indices often combine data from different species when used in monitoring programs. Heuristic properties can suggest preferred indices, but we lack objective ways to discriminate between indices with similar heuristics. Biodiversity indices can be evaluated by determining how well they reflect management objectives that a monitoring program aims to support. For example, the Convention on Biological Diversity requires reporting about extinction rates, so simple indices that reflect extinction risk would be valuable. We developed 3 biodiversity indices that are based on simple models of population viability that relate extinction risk to abundance. We based the first index on the geometric mean abundance of species and the second on a more general power mean. In a third index, we integrated the geometric mean abundance and trend. These indices require the same data as previous indices, but they also relate directly to extinction risk. Field data for butterflies and woodland plants and experimental studies of protozoan communities show that the indices correlate with local extinction rates. Applying the index based on the geometric mean to global data on changes in avian abundance suggested that the average extinction probability of birds has increased approximately 1% from 1970 to 2009., Los índices de biodiversidad combinan frecuentemente los datos de diferentes especies cuando se usan en los programas de monitoreo. Las propiedades heurísticas pueden sugerir índices preferidos, pero carecemos de medios objetivos para discriminar a los índices con propiedades heurísticas similares. Los índices de biodiversidad pueden evaluarse al determinar qué tan bien reflejan los objetivos de manejo que un programa de monitoreo busca apoyar. Por ejemplo, la Convención sobre la Diversidad Biológica requiere reportar las tasas de extinción, así que los índices que reflejan el riesgo de extinción serían valiosos. Desarrollamos 3 índices de biodiversidad que se basan en modelos sencillos de viabilidad de población y que relacionan el riesgo de extinción con la abundancia. Basamos el primer índice en la media geométrica de la abundancia de especies, y el segundo en una media de poder m´as general. En el tercer índice integramos la media geométrica y la tendencia. Estos índices requieren los mismos datos que índices previos, pero también se relacionan directamente con el riesgo de extinci´on. La información de campo sobre mariposas y plantas de bosque, y los estudios experimentales de comunidades protozoarias, muestran que los índices se correlacionan con las tasas locales de extinción. Al aplicar el índice basado en la media geométrica sobre los datos globales de los cambios en la abundancia de aves, sugirió que la probabilidad de extinción promedio de aves ha incrementado aproximadamente 1% desde 1970 hasta 2009.