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Recent developments in complex systems analysis have led to new techniques for detecting causal relationships using relatively short time series, on the order of 30 sequential observations. Although many ecological observation series are even shorter, perhaps fewer than ten sequential observations, these shorter time series are often highly replicated in space (i.e., plot replication). Here, we combine the existing techniques of convergent cross mapping (CCM) and dewdrop regression to build a novel test of causal relations that leverages spatial replication, which we call multispatial CCM. Using examples from simulated and real-world ecological data, we test the ability of multispatial CCM to detect causal relationships between processes. We find that multispatial CCM successfully detects causal relationships with as few as five sequential observations, even in the presence of process noise and observation error. Our results suggest that this technique may constitute a useful test for causality in systems where experiments are difficult to perform and long time series are not available. This new technique is available in the multispatialCCM package for the R programming language.

At the global scale, human activities are threatening the extinction of many species. It remains debated, however, whether there has been corresponding loss of biodiversity at the smaller spatial scales at which species loss often erodes ecosystem functioning, stability and services. Here we consider changes in local biodiversity and productivity over 37 years in 21 grasslands and savannahs with known agricultural land-use histories. We show that, during the century following agricultural abandonment, local plant diversity recovers only incompletely and plant productivity does not significantly recover. By 91 years after agricultural abandonment, despite many local species gains, formerly ploughed fields still had only three quarters of the plant diversity and half of the plant productivity observed in a nearby remnant ecosystem that has never been ploughed. The large and growing extent of recovering ecosystems provides an unprecedented opportunity to reverse the impacts of habitat loss. Active restoration efforts are needed to enable and accelerate recovery. Analysing changes in grasslands and savannahs following agricultural abandonment, the authors show that even after more than 90 years, plant diversity and productivity recovered by only 73% and 53%, respectively.

Ecological stability is a vital component of natural ecosystems that can inform effective conservation and ecosystem management. Furthermore, there is increasing interest in making comparisons of stability values across sites, systems and taxonomic groups, often using comparative synthetic approaches, such as meta‐analysis. However, these synthetic approaches often compare/contrast systems where measures of stability mean very different things to the taxa involved. Here, we present results from theoretical models and empirical data to illustrate how differences in growth rates among taxa influence four widely used metrics of ecological stability of species abundances responding to pulse perturbations: resilience, recovery, resistance and temporal stability. We refer to these classic growth‐rate‐dependent metrics as ‘realised’ stability. We show that realised resilience and realised temporal stability vary as a function of organisms' growth rates; realised recovery depends on the relation between growth rate and sampling duration; and realised resistance depends on the relation between growth rate and sampling interval. To account for these influences, we introduce metrics intended to be more independent of growth rates, which we refer to as ‘intrinsic’ stability. Intrinsic stability can be used to summarise the overall effects of a disturbance, separately from internal recovery processes – thereby allowing more general comparisons of disturbances across organisms and contexts. We argue that joint consideration of both realised and intrinsic stability is important for future comparative studies. We present results from theoretical models and empirical data to illustrate how differences in growth rates among taxa influence four widely used metrics of ecological stability of species abundances responding to pulse perturbations: resilience, recovery, resistance and temporal stability. To account for these influences on growth rate, we introduce ‘intrinsic’ stability metrics. Intrinsic stability can be used to summarise the overall effects of a disturbance, separately from internal recovery processes – thereby allowing more general comparisons of disturbances across organisms and contexts.

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 embedding, and particle filters were able to: (1) correctly quantify the contributions of deterministic versus stochastic variability; (2) successfully estimate “true” abundance dynamics; and (3) correctly forecast time to extinction. Our results therefore demonstrate the importance of accounting for effects of complex, nonstatic dynamics in studies of ecological stability and provide an empirically tractable and flexible toolkit for conducting these measurements.

1. Dispersal and density dependence are major determinants of spatial structure, population dynamics and coexistence for tropical forest plants. However, because these two processes can jointly influence spatial structure at similar scales, analysing spatial patterns to separate and quantify them is often difficult. 2. Species functional traits can be useful indicators of dispersal and density dependence. However, few methods exist for linking functional traits to quantitative estimates of these processes that can be compared across multiple species. 3. We analysed static spatial patterns of woody plant populations in the 50 ha Forest Dynamics Plot on Barro Colorado Island, Panama with methods that distinguished scale-specific differences in species aggregation. We then tested how these differences related to seven functional traits: growth form, dispersal syndrome, tree canopy layer, adult stature, seed mass, wood density and shade tolerance. Next, we fit analytically tractable spatial moment models to the observed spatial structure of species characterized by similar trait values, which allowed us to estimate relationships of functional traits with the spatial scale of dispersal, and the spatial scale and intensity of negative density dependence. 4. Our results confirm that lianas are more aggregated than trees, and exhibit increased aggregation within canopy gaps. For trees, increased seed mass, wood density and shade tolerance were associated with less intense negative density dependence, while higher canopy layers and increased stature were associated with decreased aggregation and better dispersal. Spatial structure for trees was also strongly determined by dispersal syndrome. Averaged across all spatial scales, zoochory was more effective than wind dispersal, which was more effective than explosive dispersal. However, at intermediate scales, zoochory was associated with more aggregation than wind dispersal, potentially because of differences in short-distance dispersal and the intensity of negative density dependence. 5. Synthesis. We develop new tools for identifying significant associations between functional traits and spatial structure, and for linking these associations to quantitative estimates of dispersal scale and the strength and scale of density dependence. Our results help clarify how these processes influence woody plant species on Barro Colorado, and demonstrate how these tools can be applied to other sites and systems.

The importance of microclimate conditions is becoming increasingly recognised in ecological research, especially as they can differ widely from macro‐ and mesoclimate. Effective measurement of microclimate variability in heterogeneous field conditions requires measurements from a large number of sensors, ideally sampling across both small and large scales, which necessitates the development of cost‐effective sensor networks. Here, we develop an environmental microcontroller ( EMU ) sensor network for measuring soil moisture and temperature with high spatiotemporal coverage. The system can easily be implemented in larger or smaller setups, though our study consisted of 40 plots distributed across 4 sites in northern Lapland. Using 40 EMUs , each equipped with 10 soil temperature and 10 soil moisture sensors, we collected roughly 3.5 million unique soil temperature and moisture records over the course of about 2 months. We then compared these measurements to those from commercial temperature sensors ( iButtons ) and TDR soil moisture probes. We show that our sensor network is able to successfully characterise microclimate variation at the site, plot and within‐plot scales with high accuracy and good reliability. Moreover, even including development costs, the total price per EMU unit was less than €100, yielding a much cheaper and higher resolution system than is currently available from commercial producers. For both temperature and soil moisture, we found a strong relationship between the sensor network measurement and those obtained from commercial systems. Roughly 10% of the overall spatial variation in temperature and moisture occurred within plots—that is. over the scale of 25–50 cm. Our results show that accurate measurements of microclimate soil temperature and soil moisture can be obtained using low‐cost sensor networks. We also show that microclimate variability at small scales can make up a considerable fraction of total environmental variability. To this end, we discuss some of the difficulties encountered during the development process and suggest possible improvements for further studies. While the sensor network described here can be applied without any modifications for quantifying topsoil conditions, we note that with minor changes to our code and hardware, the system can be customised for other kinds of measurements.

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. A biodiversity–ecosystem functioning experiment in a young subtropical forest shows that, at high species richness, directed but not random species loss leads to pronounced productivity decrease.

For many taxa and systems, species richness peaks at midelevations. One potential explanation for this pattern is that large-scale changes in climate and geography have, over evolutionary time, selected for traits that are favored under conditions found in contemporary midelevation regions. To test this hypothesis, we use records of historical temperature and topographic changes over the past 65 Myr to construct a general simulation model of plethodontid salamander evolution in eastern North America. We then explore possible mechanisms constraining species to midelevation bands by using the model to predict plethodontid evolutionary history and contemporary geographic distributions. Our results show that models that incorporate both temperature and topographic changes are better able to predict these patterns, suggesting that both processes may have played an important role in driving plethodontid evolution in the region. Additionally, our model (whose annotated source code is included as a supplement) represents a proof of concept to encourage future work that takes advantage of recent advances in computing power to combine models of ecology, evolution, and earth history to better explain the abundance and distribution of species over time.

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.

1. \"Old fields\" are ecosystems that have been previously managed and subsequently abandoned, usually from agricultural use. These systems are classic testing grounds for hypotheses about community assembly. However, old field succession can be difficult to predict: seemingly similar fields often diverge in terms of species composition and environmental conditions. 2. Here, we test the relative roles of contingency and stochasticity in driving vegetative successional dynamics. We draw on three decades of surveys in 24 old fields at the Cedar Creek Ecosystem Science Reserve (Minnesota, USA), and focus on five drivers that are known to shape local plant communities: soil fertility, fire, climate, competition, and demography. These drivers can contribute to contingency when they act consistently across fields and years (e.g., soil nitrogen accumulation, experimental fire regimes, or average climate), or to stochasticity when their effects are variable (e.g., annual variations in weather, or colonization and mortality events). 3. We proceed in two steps. First, we fit regressions estimating abundance, colonization, and mortality for eight major functional groups in relation to these five drivers. We then use these regressions to parameterize a series of metacommunity simulation models, and test whether observed levels of stochasticity and variation in the drivers are sufficient to explain successional divergence. 4. All drivers were significantly associated with plant species abundances, colonization, and mortality. Contingent factors strongly altered predicted successional trajectories. However, replicate simulations with similar conditions followed similar successional trajectories, suggesting that stochastic processes did not lead to divergence. This robustness of successional dynamics may be explained by compensatory trade-offs. For example, species that were abundant late in succession typically suffered from low colonization rates and high mortality rates early in succession. 5. Synthesis. Average successional dynamics among old fields at Cedar Creek follow largely consistent trends. Though dynamics of individual fields vary, much of this variation can be explained by contingent factors. Stochastic processes appear not to be sufficiently strong to create divergent successional trajectories among fields with similar sets of drivers. Our results therefore suggest that divergence among successional trajectories in chronosequences may be the result of predictable contingent factors, rather than unpredictable stochastic fluctuations.