Search

Despite substantial progress in understanding global biodiversity loss, major taxonomic and geographic knowledge gaps remain. Decision makers often rely on expert judgement to fill knowledge gaps, but are rarely able to engage with sufficiently large and diverse groups of specialists. To improve understanding of the perspectives of thousands of biodiversity experts worldwide, we conducted a survey and asked experts to focus on the taxa and freshwater, terrestrial, or marine ecosystem with which they are most familiar. We found several points of overwhelming consensus (for instance, multiple drivers of biodiversity loss interact synergistically) and important demographic and geographic differences in specialists’ perspectives and estimates. Experts from groups that are underrepresented in biodiversity science, including women and those from the Global South, recommended different priorities for conservation solutions, with less emphasis on acquiring new protected areas, and provided higher estimates of biodiversity loss and its impacts. This may in part be because they disproportionately study the most highly threatened taxa and habitats.

Ecological stability refers to a range of concepts used to quantify how species and environments change over time and in response to disturbances. Most empirically tractable ecological stability metrics assume that systems have simple dynamics and static equilibria. However, ecological systems are typically complex and often lack static equilibria (e.g., predator–prey oscillations, transient dynamics, chaos). Failing to account for these factors can lead to biased estimates of stability, in particular, by conflating effects of observation error, process noise, and underlying deterministic dynamics. To distinguish among these processes, we combine three existing approaches: state space models; delay embedding methods; and particle filtering. Jointly, these provide something akin to a deterministically “detrended” version of the coefficient of variation, separately tracking variability due to deterministic dynamics versus stochastic perturbations. Moreover, these variability estimates can be used to forecast dynamics, classify underlying sources of stochastic dynamics, and estimate the “exit time” before a state change takes place (e.g., local extinction events). Importantly, the time-delay embedding methods that we employ make very few assumptions about the functions governing deterministic dynamics, which facilitates applications in systems with limited data and a priori biological knowledge. To demonstrate how complex dynamics without static equilibria can bias ecological stability estimates, we analyze simulated time series of abundance dynamics in a system with time-varying carrying capacity and empirically observed abundance dynamics of the green algae Chlamydomonas terricola grown in a diverse microcosm mixture under variable temperature conditions. We show that stability estimates based on raw observations greatly overestimate temporal variability and fail to accurately forecast time to extinction. In contrast, joint application of state space modeling, delay, Deutsche Forschungsgemeinschaft 848/26-1, Version of record

The original version of this Article contained errors in the Methods section ‘Target causal effect’, in which terms were omitted from the mathematical definitions of the causal effect and average causal effect. These sentences incorrectly read “The causal effect of a change in richness from R′ to R″ on productivity P in plot i is defined as [(R″) − (R′)], where Pi(R″) is the potential productivity outcome when R = R″ and P(R′) is the potential productivity outcome when R = R′ (R′ ≠ R″).” and “The average causal effect of a change in biodiversity from R′ to R″ across all plots is [(R″) − P(R′)], where E[·] is the expectation operator.”. The correct version states “[Pi(R′′) − Pi(R′)]” in place of “[(R″) − (R′)]”, “Pi (R′)” in place of “P (R′)”, and “E[Pi(R′′) − Pi(R′)]” in place of “[(R″) − P(R′)]”. This has been corrected in both the PDF and HTML versions of the Article.

Causal effects of biodiversity on ecosystem functions can be estimated using experimental or observational designs - designs that pose a tradeoff between drawing credible causal inferences from correlations and drawing generalizable inferences. Here, we develop a design that reduces this tradeoff and revisits the question of how plant species diversity affects productivity. Our design leverages longitudinal data from 43 grasslands in 11 countries and approaches borrowed from fields outside of ecology to draw causal inferences from observational data. Contrary to many prior studies, we estimate that increases in plot-level species richness caused productivity to decline: a 10% increase in richness decreased productivity by 2.4%, 95% CI [-4.1, -0.74]. This contradiction stems from two sources. First, prior observational studies incompletely control for confounding factors. Second, most experiments plant fewer rare and non-native species than exist in nature. Although increases in native, dominant species increased productivity, increases in rare and non-native species decreased productivity, making the average effect negative in our study. By reducing the tradeoff between experimental and observational designs, our study demonstrates how observational studies can complement prior ecological experiments and inform future ones.

Aims Intensive land management practices can compromise soil biodiversity, thus jeopardizing long-term soil productivity. Arbuscular mycorrhizal fungi (AMF) play a pivotal role in promoting soil productivity through obligate symbiotic associations with plants. However, it is not clear how properties of plant communities, especially species richness and composition influence the viability of AMF populations in soils. Methods Here we test whether monocultures of eight plant species from different plant functional groups, or a diverse mixture of plant species, maintain more viable AMF propagules. To address this question, we extracted AMF spores from 12-year old plant monocultures and mixtures and paired single AMF spores with single plants in a factorial design crossing AMF spore origin with plant species identity. Important Findings AMF spores from diverse plant mixtures were more successful at colonizing multiple plant species and plant individuals than AMF spores from plant monocultures. Furthermore, we found evidence that AMF spores originating from diverse mixtures more strongly increased biomass than AMF from monocultures in the legume Trifolium repens L. AMF viability and ability to interact with many plant species were greater when AMF spores originated from 12-year old mixtures than monocultures. Our results show for the first time that diverse plant communities can sustain AMF viability in soils and demonstrate the potential of diverse plant communities to maintain viable AMF propagules that are a key component to soil health and productivity.

Models of natural processes necessarily sacrifice some realism for the sake of tractability. Detailed, parameter‐rich models often provide accurate estimates of system behaviour but can be data‐hungry and difficult to operationalize. Moreover, complexity increases the danger of ‘over‐fitting’, which leads to poor performance when models are applied to novel conditions. This challenge is typically described in terms of a trade‐off between bias and variance (i.e. low accuracy vs. low precision). In studies of ecological communities, this trade‐off often leads to an argument about the level of detail needed to describe interactions among species. Here, we used data from a grassland biodiversity experiment containing nine locally abundant plant species (the Jena ‘dominance experiment’) to parameterize models representing six increasingly complex hypotheses about interactions. For each model, we calculated goodness‐of‐fit across different subsets of the data based on sown species richness levels, and tested how performance changed depending on whether or not the same data were used to parameterize and test the model (i.e. within vs. out‐of‐sample), and whether the range of diversity treatments being predicted fell inside or outside of the range used for parameterization. As expected, goodness‐of‐fit improved as a function of model complexity for all within‐sample tests. In contrast, the best out‐of‐sample performance generally resulted from models of intermediate complexity (i.e. with only two interaction coefficients per species—an intraspecific effect and a single pooled interspecific effect), especially for predictions that fell outside the range of diversity treatments used for parameterization. In accordance with other studies, our results also demonstrate that commonly used selection methods based on AIC of models fitted to the full dataset correspond more closely to within‐sample than out‐of‐sample performance. Synthesis. Our results demonstrate that models which

Ecological stability refers to a range of concepts used to quantify how species and environments change over time and in response to disturbances. Most empirically tractable ecological stability metrics assume that systems have simple dynamics and static equilibria. However, ecological systems are typically complex and often lack static equilibria (e.g., predator–prey oscillations, transient dynamics, chaos). Failing to account for these factors can lead to biased estimates of stability, in particular, by conflating effects of observation error, process noise, and underlying deterministic dynamics. To distinguish among these processes, we combine three existing approaches: state space models; delay embedding methods; and particle filtering. Jointly, these provide something akin to a deterministically “detrended” version of the coefficient of variation, separately tracking variability due to deterministic dynamics versus stochastic perturbations. Moreover, these variability estimates can be used to forecast dynamics, classify underlying sources of stochastic dynamics, and estimate the “exit time” before a state change takes place (e.g., local extinction events). Importantly, the time-delay embedding methods that we employ make very few assumptions about the functions governing deterministic dynamics, which facilitates applications in systems with limited data and a priori biological knowledge. To demonstrate how complex dynamics without static equilibria can bias ecological stability estimates, we analyze simulated time series of abundance dynamics in a system with time-varying carrying capacity and empirically observed abundance dynamics of the green algae Chlamydomonas terricola grown in a diverse microcosm mixture under variable temperature conditions. We show that stability estimates based on raw observations greatly overestimate temporal variability and fail to accurately forecast time to extinction. In contrast, joint application of state space modeling, delay

Despite substantial progress in understanding global biodiversity loss, major taxonomic and geographic knowledge gaps remain. Decision makers often rely on expert judgement to fill knowledge gaps, but are rarely able to engage with sufficiently large and diverse groups of specialists. To improve understanding of the perspectives of thousands of biodiversity experts worldwide, we conducted a survey and asked experts to focus on the taxa and freshwater, terrestrial, or marine ecosystem with which they are most familiar. We found several points of overwhelming consensus (for instance, multiple drivers of biodiversity loss interact synergistically) and important demographic and geographic differences in specialists’ perspectives and estimates. Experts from groups that are underrepresented in biodiversity science, including women and those from the Global South, recommended different priorities for conservation solutions, with less emphasis on acquiring new protected areas, and provided higher estimates of biodiversity loss and its impacts. This may in part be because they disproportionately study the most highly threatened taxa and habitats.

Global change drivers, such as anthropogenic nutrient inputs, are increasing globally. Nutrient deposition simultaneously alters plant biodiversity, species composition and ecosystem processes like aboveground biomass production. These changes are underpinned by species extinction, colonisation and shifting relative abundance. Here, we use the Price equation to quantify and link the contributions of species that are lost, gained or that persist to change in aboveground biomass in 59 experimental grassland sites. Under ambient (control) conditions, compositional and biomass turnover was high, and losses (i.e. local extinctions) were balanced by gains (i.e. colonisation). Under fertilisation, the decline in species richness resulted from increased species loss and decreases in species gained. Biomass increase under fertilisation resulted mostly from species that persist and to a lesser extent from species gained. Drivers of ecological change can interact relatively independently with diversity, composition and ecosystem processes and functions such as aboveground biomass due to the individual contributions of species lost, gained or persisting.

Cheating in microbial communities is often regarded as a precursor to a “tragedy of the commons,” ultimately leading to over-exploitation by a few species and destabilization of the community. While current evidence suggests that cheaters are evolutionarily and ecologically abundant, they can also play important roles in communities, such as promoting cooperative behaviors of other species. We developed a closed culture model with two microbial species and a single, complex nutrient substrate (the metaphorical “common”). One of the organisms, an enzyme producer, degrades the substrate, releasing an essential and limiting resource that it can use both to grow and produce more enzymes, but at a cost. The second organism, a cheater, does not produce the enzyme but can access the diffused resource produced by the other species, allowing it to benefit from the public good without contributing to it. We investigated evolutionarily stable states of coexistence between the two organisms and described how enzyme production rates and resource diffusion influence organism abundances. Our model shows that, in the long-term evolutionary scale, monocultures of the producer species drive themselves extinct because selection always favors mutant invaders that invest less in enzyme production, ultimately driving down the release of resources. However, the presence of a cheater buffers this process by reducing the fitness advantage of lower enzyme production, thereby preventing runaway selection in the producer, and promoting coexistence. Resource diffusion rate controls cheater growth, preventing it from outcompeting the producer. These results show that competition from cheaters can force producers to maintain adequate enzyme production to sustain both itself and the cheater. This is similar to what is known in evolutionary game theory as a “snowdrift game” – a metaphor describing a snow shoveler and a cheater following in their clean tracks. We move further to show that cheating c

Improved understanding of complex dynamics has revealed insights across many facets of ecology, and has enabled improved forecasts and management of future ecosystem states. However, an enduring challenge in forecasting complex dynamics remains the differentiation between complexity and stochasticity, that is, to determine whether declines in predictability are caused by stochasticity, nonlinearity, or chaos. Here, we show how to quantify the relative contributions of these factors to prediction error using Georgii Gause's iconic predator–prey microcosm experiments, which, critically, include experimental replicates that differ from one another only in initial abundances. We show that these differences in initial abundances interact with stochasticity, nonlinearity, and chaos in unique ways, allowing us to identify the impacts of these factors on prediction error. Our results suggest that jointly analyzing replicate time series across multiple, distinct starting points may be necessary for understanding and predicting the wide range of potential dynamic types in complex ecological systems., Version of record

AbstractEcological stability refers to a family of concepts used to describe how systems of interacting species vary through time and respond to disturbances. Because observed ecological stability depends on sampling scales and environmental context, it is notoriously difficult to compare measurements across sites and systems. Here, we apply stochastic dynamical systems theory to derive general statistical scaling relationships across time, space, and ecological level of organisation for three fundamental stability aspects: resilience, resistance, and invariance. These relationships can be calibrated using random or representative samples measured at individual scales, and projected to predict average stability at other scales across a wide range of contexts. Moreover deviations between observed vs. extrapolated scaling relationships can reveal information about unobserved heterogeneity across time, space, or species. We anticipate that these methods will be useful for cross‐study synthesis of stability data, extrapolating measurements to unobserved scales, and identifying underlying causes and consequences of heterogeneity.

Ecological stability refers to a family of concepts used to describe how systems of interacting species vary through time and respond to disturbances. Because observed ecological stability depends on sampling scales and environmental context, it is notoriously difficult to compare measurements across sites and systems. Here, we apply stochastic dynamical systems theory to derive general statistical scaling relationships across time, space, and ecological level of organisation for three fundamental stability aspects: resilience, resistance, and invariance. These relationships can be calibrated using random or representative samples measured at individual scales, and projected to predict average stability at other scales across a wide range of contexts. Moreover deviations between observed vs. extrapolated scaling relationships can reveal information about unobserved heterogeneity across time, space, or species. We anticipate that these methods will be useful for cross-study synthesis of stability data, extrapolating measurements to unobserved scales, and identifying underlying causes and consequences of heterogeneity., Funding Agencies|TULIP French Laboratory of Excellence [ANR-10-LABX-41, ANR-11-IDEX-0002-02]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [VR 2017-05245]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [FZT 118]; NSFNational Science Foundation (NSF) [EPS-1655726]; Horizon 2020 Framework Programme [666971]

Community composition is a primary determinant of how biodiversity change influences ecosystem functioning and, therefore, the relationship between biodiversity and ecosystem functioning (BEF). We examine the consequences of community composition across six structurally realistic plant community models. We find that a positive correlation between species’ functioning in monoculture versus their dominance in mixture with regard to a specific function (the “function-dominance correlation”) generates a positive relationship between realised diversity and ecosystem functioning across species richness treatments. However, because realised diversity declines when few species dominate, a positive function-dominance correlation generates a negative relationship between realised diversity and ecosystem functioning within species richness treatments. Removing seed inflow strengthens the link between the function–dominance correlation and BEF relationships across species richness treatments but weakens it within them. These results suggest that changes in species’ identities in a local species pool may more strongly affect ecosystem functioning than changes in species richness.

Contains fulltext : 212276.pdf (publisher's version ) (Open Access)

1. Models of natural processes necessarily sacrifice some realism for the sake of tractability. Detailed, parameter‐rich models often provide accurate estimates of system behaviour but can be data‐hungry and difficult to operationalize. Moreover, complexity increases the danger of ‘over‐fitting’, which leads to poor performance when models are applied to novel conditions. This challenge is typically described in terms of a trade‐off between bias and variance (i.e. low accuracy vs. low precision). 2. In studies of ecological communities, this trade‐off often leads to an argument about the level of detail needed to describe interactions among species. Here, we used data from a grassland biodiversity experiment containing nine locally abundant plant species (the Jena ‘dominance experiment’) to parameterize models representing six increasingly complex hypotheses about interactions. For each model, we calculated goodness‐of‐fit across different subsets of the data based on sown species richness levels, and tested how performance changed depending on whether or not the same data were used to parameterize and test the model (i.e. within vs. out‐of‐sample), and whether the range of diversity treatments being predicted fell inside or outside of the range used for parameterization. 3. As expected, goodness‐of‐fit improved as a function of model complexity for all within‐sample tests. In contrast, the best out‐of‐sample performance generally resulted from models of intermediate complexity (i.e. with only two interaction coefficients per species—an intraspecific effect and a single pooled interspecific effect), especially for predictions that fell outside the range of diversity treatments used for parameterization. In accordance with other studies, our results also demonstrate that commonly used selection methods based on AIC of models fitted to the full dataset correspond more closely to within‐sample than out‐of‐sample performance. 4. Synthesis. Our results demonstrate that m

Point 1: The ecological models of Alfred J. Lotka and Vito Volterra have had an enormous impact on ecology over the past century. Some of the earliest—and clearest—experimental tests of these models were famously conducted by Georgy Gause in the 1930s. Although well known, the data from these experiments are not widely available and are often difficult to analyze using standard statistical and computational tools. Point 2: Here, we introduce the gauseR package, a collection of tools for fitting Lotka‐Volterra models to time series data of one or more species. The package includes several methods for parameter estimation and optimization, and includes 42 datasets from Gause's species interaction experiments and related work. Additionally, we include with this paper a short blog post discussing the historical importance of these data and models, and an R vignette with a walk‐through introducing the package methods. The package is available for download at github.com/adamtclark/gauseR. Point 3: To demonstrate the package, we apply it to several classic experimental studies from Gause, as well as two other well‐known datasets on multi‐trophic dynamics on Isle Royale, and in spatially structured mite populations. In almost all cases, models fit observations closely and fitted parameter values make ecological sense. Point 4: Taken together, we hope that the methods, data, and analyses that we present here provide a simple and user‐friendly way to interact with complex ecological data. We are optimistic that these methods will be especially useful to students and educators who are studying ecological dynamics, as well as researchers who would like a fast tool for basic analyses.

Unprecedented species loss in diverse forests indicates the urgent need to test its consequences for ecosystem functioning. However, experimental evaluation based on realistic extinction scenarios is lacking. Using species interaction networks we introduce an approach to separate effects of node loss (reduced species number) from effects of link loss or compensation (reduced or increased interspecific interactions) on ecosystem functioning along directed extinction scenarios. By simulating random and non-random extinction scenarios in an experimental subtropical Chinese forest, we find that species loss is detrimental for stand volume in all scenarios, and that these effects strengthen with age. However, the magnitude of these effects depends on the type of attribute on which the directed species loss is based, with preferential loss of evolutionarily distinct species and those from small families having stronger effects than those that are regionally rare or have high specific leaf area. These impacts were due to both node loss and link loss or compensation. At high species richness (reductions from 16 to 8 species), strong stand-volume reduction only occurred in directed but not random extinction. Our results imply that directed species loss can severely hamper productivity in already diverse young forests.

Although numerous studies show that communities are jointly influenced by predation and competitive interactions, few have resolved how temporal variability in these interactions influences community assembly and stability. Here, we addressed this challenge in experimental microbial microcosms by employing empirical dynamic modelling tools to: (1) detect causal interactions between prey species in the absence and presence of a predator; (2) quantify the time‐varying strength of these interactions and (3) explore stability in the resulting communities. Our findings show that predators boost the number of causal interactions among community members, and lead to reduced dynamic stability, but higher coexistence among prey species. These results correspond to time‐varying changes in species interactions, including emergence of morphological characteristics that appeared to reduce predation, and indirectly facilitate growth of predator‐susceptible species. Jointly, our findings suggest that careful consideration of both context and time may be necessary to predict and explain outcomes in multi‐trophic systems.

To predict shrub responses under climate change in tundra, we need to understand how thermal conditions and herbivory contribute to growth. We hypothesise that shrub growth increases with thermal conditions and precipitation, but that this increase is counteracted by insect herbivory, and that these climate‐insect herbivory relationships are modified by both browsing and plant age. We use empirical dynamic modelling (EDM) to analyse a 20‐year time series on willow (Salix phylicifolia) shoot growth, growing degree days, summer precipitation and herbivory from an experiment at forest‐tundra ecotone. The experiment includes manipulations of avian and mammal browsing (fences) and ramet age (pruning to rejuvenate willows). Negative effects of insect herbivory on willow shoot growth were intensified during warmer years, whereas increasing precipitation led to reduced effects. Moreover, the effect of insect herbivores on shoot growth varied with ramet age and vertebrate browsing: younger ramets generally experienced less negative insect herbivore effects, whereas ptarmigan browsing was associated with more positive temperature effects on shoot growth, and reindeer browsing with more negative effects of insect herbivory and precipitation. Synthesis. Our findings show that the negative effects of insect herbivory on shoot growth likely intensify under warmer thermal conditions, but that increasing precipitation can counteract these effects. Moreover, changes in thermal conditions, precipitation and vertebrate browsers all have predictable, albeit complex and nonlinear, effects on shrub growth, highlighting the importance of long‐term experimental data and flexible analytical methods such as EDM for characterizing climate and community interactions in artic systems.

Population and basic community ecology are commonly presented to students through a set of distinct models, such as those for exponential growth, logistic growth, competition, predation, and so forth. This approach mirrors the historical development of the field, but it has several shortcomings as a way to present ecological theory. First, the classical equations can appear disconnected from one another. Second, differences in the parameters and styles of the equations do not lend themselves to comparison in a common graphical form. And third, the set of equations as they are commonly presented provides no easy way to see whether any concepts are left out. In fact, something is left out that is not commonly taught: the concept of faster-than-exponential growth approaching a singularity, which is important for understanding rapidly growing systems. In the present article, we demonstrate a unified approach that simplifies the traditional equations of ecology, expands their scope, and emphasizes their interconnections.

Global biodiversity is declining at rates faster than at any other point in human history. Experimental manipulations at small spatial scales have demonstrated that communities with fewer species consistently produce less biomass than higher diversity communities. Understanding the consequences of the global extinction crisis for ecosystem functioning requires understanding how local experimental results are likely to change with increasing spatial and temporal scales and from experiments to naturally assembled systems.Scaling across time and space in a changing world requires baseline predictions. Here, we provide a graphical null model for area scaling of biodiversity‐ecosystem functioning relationships using observed macroecological patterns: the species area curve and the biomass area curve. We use species‐area and biomass‐area curves to predict how species richness – biomass relationships are likely to change with increasing sampling extent. We then validate these predictions with data from two naturally assembled ecosystems: a Minnesota savanna and a Panamanian tropical dry forest.Our graphical null model predicts that biodiversity‐ecosystem functioning relationships are scale dependent. However, we note two important caveats. First, our results indicate an apparent contradiction between predictions based on measurements in biodiversity‐ecosystem functioning experiments and from scaling theory. When ecosystem functioning is measured as per unit area (e.g., biomass m‐2), as is common in biodiversity‐ecosystem functioning experiments, the slope of the biodiversity ecosystem functioning relationship should decrease with increasing scale. Alternatively, when ecosystem functioning is not measured per unit area (e.g., summed total biomass), as is common in scaling studies, the slope of the biodiversity‐ecosystem functioning relationship should increase with increasing spatial scale. Second, the underlying macroecological patterns of biodiversity experiments are pred