As invasive species spread, the ability of local communities to resist invasion depends on the strength of biotic interactions. Evolutionarily unused to the invader, native predators or herbivores may be initially wary of consuming newcomers, allowing them to proliferate. However, these relationships may be highly dynamic, and novel consumer-resource interactions could form as familiarity grows. Here, we explore the development of effective biotic resistance towards a highly invasive alga using multiple space-for-time approaches. We show that the principal native Mediterranean herbivore learns to consume the invader within less than a decade. At recently invaded sites, the herbivore actively avoided the alga, shifting to distinct preference and high consumptions at older sites. This rapid strengthening of the interaction contributed to the eventual collapse of the alga after an initial dominance. Therefore, our results stress the importance of conserving key native populations to allow communities to develop effective resistance mechanisms against invaders.

The potential for forecasting the dynamics of ecological systems is currently unclear, with contrasting opinions regarding its feasibility due to ecological complexity. To investigate forecast skill within and across system complexity, we monitored a microbial system exposed to either constant or fluctuating temperatures in a five months long laboratory experiment. We tested how forecasting of species abundances depends on number and strength of interactions and on model size (number of predictors). We also tested how greater system complexity (i.e. the fluctuating temperatures) impacted these relations. We found that the more a species interacted, the weaker these interactions were and the better its abundance was predicted. Forecast skill increased with model size. Greater system complexity decreased forecast skill for three out of eight species. These insights into how abundance prediction depends on the embedding of the species within the system and on overall system complexity could improve species forecasting and monitoring.

Primary consumers in aquatic ecosystems are frequently limited by the quality of their food, often expressed as phytoplankton elemental and biochemical composition. Effects of these food quality indicators vary across studies, and the relative importance of elemental (nitrogen and phosphorus) versus biochemical (fatty acid and sterol) limitation in aquatic food webs has been debated. Here we present results of a meta-analysis using >100 experimental studies, which confirms that limitation by N, P, essential fatty acids, and sterols all have significant negative effects on zooplankton performance. However, effects varied by grazer response (growth versus reproduction), specific manipulation, and across taxa. P limitation had greater effects on zooplankton growth than fatty acids, but P and fatty acid limitation had equal effects on reproduction. Furthermore, we show that nutrient co-limitation in zooplankton occurs, that indirect effects induced by P limitation exceed direct effects of mineral P limitation, that effects of nutrient amendments using laboratory phytoplankton isolates exceed those using natural field communities, and that algal physiology mediates zooplankton responses to nutrient limitation. Our meta-analysis reconciles contrasting views about the role of various food quality indicators, and their interactions, for zooplankton performance, and provides a mechanistic understanding of how environmental change affects trophic transfer.

Understanding the factors affecting thermal tolerance is crucial for predicting the impact climate change will have on ectotherms. However, the role developmental plasticity plays in allowing populations to cope with thermal extremes is poorly understood. Here, we meta-analyse how thermal tolerance is acutely and persistently impacted by early thermal environments by using data from 150 experimental studies on 138 ectothermic species. Thermal tolerance only increased by 0.13°C per 1°C change in developmental temperature and substantial variation in plasticity (~36%) was the result of shared evolutionary history and species ecology. Aquatic ectotherms were more than three times as plastic as terrestrial ectotherms. Notably, embryos expressed weaker but more heterogenous plasticity than older life stages, with numerous responses appearing as non-adaptive. While we did not find universal evidence for developmental temperatures to have persistent effects on thermal tolerance, persistent effects were vastly under-studied, and their direction and magnitude varied with ontogeny. Embryonic stages may represent a critical window of vulnerability to changing environments and we urge researchers to consider early life stages when assessing the climate vulnerability of ectotherms. Overall, our synthesis suggests that developmental changes in thermal tolerance will rarely reach levels of perfect compensation and buffer ectotherms from rising temperatures.

The storage effect is a general explanation for coexistence in a variable environment. The generality of the storage effect is both a strength — it can be quantified in many systems — and a challenge — there is not a clear relationship between the abstract conditions for storage effect and species’ life-history traits (e.g., dormancy, stage-structure, non-overlapping generations), thus precluding a simple ecological interpretation of the storage effect. Our goal here is to provide a clearer understanding of the conditions for the storage effect as a step towards a better general explanation for coexistence in a variable environment. Our approach focuses on dividing one of the key conditions for the storage effect, covariance between environment and competition, into two pieces, namely that there must be a causal relationship between environment and competition, and that the effects of the environment do not change too quickly. This finer-grained definition can explain a number of previous results, including 1) that the storage effect promotes annual plant coexistence when the germination rate fluctuates, but not when the seed yield fluctuates, 2) that the storage effect is more likely to be induced by resource competition than apparent competition, and 3) that the spatial storage effect is more probable than the temporal storage effect. Additionally, our expanded definition suggests two novel mechanisms by which the temporal storage effect can arise: transgenerational plasticity, and causal chains of environmental variables. These mechanisms produce coexistence via the storage effect without any need for stage structure or a temporally autocorrelated environment.

Ecologists often rely on observational data to understand causal relationships. Although observational causal inference methodologies exist, model selection based on information criterion (e.g., AIC) remains a common approach used to understand ecological relationships. However, such approaches are meant for predictive inference and is not appropriate for drawing causal conclusions. Here, we highlight the distinction between predictive and causal inference and show how model selection techniques can lead to biased causal estimates. Instead, we encourage ecologists to apply the backdoor criterion, a graphical rule that can be used to determine causal relationships across observational studies.

Forecasting the trajectories of species assemblages in response to ongoing climate change requires quantifying the time lags in the demographic and ecological processes through which climate impacts species' abundances. Since experimental climate manipulations are typically abrupt, the observed species responses may not match their responses to gradual climate change. We addressed this problem by transplanting alpine grassland turfs to lower elevations, recording species' demographic responses to climate and competition, and using these data to parameterize community dynamics models forced by scenarios of gradual climate change. We found that shifts in community structure following an abrupt climate manipulation were not simply accelerated versions of shifts expected under gradual warming, especially when they missed the rise of species benefiting from moderate warming. Time lags in demography and species interactions controlled the pace and trajectory of changing species' abundances under simulated 21st century climate change, and thereby prevented immediate diversity loss.

Many species-rich ecological communities result from adaptive radiation events. The effects of these explosive speciation events on community assembly remain poorly understood. Here, we explore the well-documented radiations of African cichlid fishes and interactions with their flatworm gill parasites (Cichlidogyrus spp.) including 10529 reported infections and 477 different host-parasite combinations collected through a survey of peer-reviewed literature. We assess the evolutionary, ecological, and morphological parameters on meta-communities partially affected by adaptive radiation evens using network metrics, host repertoire measures, and network link prediction (NLP). The hosts’ evolutionary history mostly determined host repertoires. Ecological and evolutionary parameters predicted host-parasite associations, but many interactions remain undetected according to NLP. Parasite meta-communities under host adaptive radiation are more specialised and stable while ecological opportunity and ecological fitting have shaped interactions elsewhere. The cichlid-Cichlidogyrus network is a suitable eco-evolutionary study system but future studies should validate our findings in other radiating host-parasite systems.

The dynamics of cyclic populations distributed in space result from the relative strength of synchronising influences and the limited dispersal of destabilising factors (activators and inhibitors), known to cause multi-annual population cycles. However, while each of these have been well studied in isolation, there is limited empirical evidence about how the processes of synchronisation and activation-inhibition act together, largely owing to the scarcity of datasets with sufficient spatial and temporal scale. We assessed a variety of models that could be underlying the spatio-temporal pattern, designed to capture both theoretical and empirical understandings of travelling waves using large-scale (> 35,000 km2), multi-year (2011-2017) field monitoring data on abundances of common vole (Microtus arvalis), a cyclic agricultural rodent pest. We found most support for a pattern formed from the summation of two radial travelling waves with contrasting speeds that together describe population growth rates across the region.

Marine microbial ecosystems underpin global biogeochemical cycles and play a central role in the regulation of Earth's climate. These communities are extremely diverse, and their taxonomic composition varies considerably across ocean basins. It has however been difficult to establish links between taxonomic diversity and ecosystem function, and the ecological and evolutionary mechanisms underpinning taxonomic variation are not well understood. Here we use an individual-based eco-evolutionary model in which taxonomic diversity emerges as a consequence of evolutionary history. Using this model we are able to show that virtually unlimited genetic divergence can be supported in highly abundant and rapidly evolving assemblages, even in the absence of niche separation. With a steady stream of genetic, epigenetic and plastic heritable changes to phenotype, competitive exclusion may be weakened, allowing sustained coexistence of nearly neutral phenotypes with highly divergent lineages. This response may help to explain observed patterns of taxonomic diversity and functional redundancy - without recourse to hidden dimensions of niche partitioning. In light of these results we suggest that individual-level variability is a key driver of species coexistence and the maintenance of microbial biodiversity.

Global change is altering patterns of community assembly, with net outcomes dependent on species’ responses to the environment, both directly and mediated through biotic interactions. Here, we assess alpine plant community responses in a 15-year factorial nitrogen addition, warming and snow manipulation experiment. We used a dynamic competition model to estimate the density-dependent and independent processes underlying changes in species-group abundances over time. Density-dependent shifts in competitive interactions drove long-term changes in abundance of species-groups under global change. Density-independent processes were important when counteracting environmental drivers limited the growth response of the dominant species. Furthermore, competitive interactions shifted with environmental change, primarily with nitrogen, and drove non-linear abundance responses across environmental gradients. Our results highlight that global change can either reshuffle species hierarchies or further favor already dominant species; predicting which outcome will occur requires incorporating both density-dependent and independent mechanisms and how they interact across multiple global change factors.

Predicting the impacts of multiple stressors is important for informing ecosystem management, but is impeded by a lack of a general framework for predicting whether stressors interact synergistically, additively, or antagonistically. Here we use process-based models to study how interactions generalise across three levels of bio-logical organisation (physiological, population, and community) for a simulated two-stressor experiment on a seagrass model system. We found that the same underlying processes could result in synergistic, additive or antagonistic interactions, with interaction type depending on initial conditions, experiment duration, stressor dynamics, and consumer presence. Our results help explain why meta-analyses of multiple stressor experimental results have struggled to identify predictors of consistently non-additive interactions in the natural environment. Experiments run over longer temporal scales, with treatments across gradients of stressor magnitude, are needed to identify the processes that underpin how stressors interact and provide useful predictions to management.

We introduce a new approach—acoustic restoration—focusing on the applied utility of soundscapes for restoration, recognizing the rich ecological and social values they encapsulate. Broadcasting soundscapes in disturbed areas can accelerate recolonization of animals and the microbes and propagules they carry; long duration recordings are also ideal sources of data for benchmarking restoration initiatives and evocative engagement tools.

Harvesting can magnify the destabilizing effects of environmental perturbations on population dynamics and, thereby, increase extinction risk. However, population-dynamic theory predicts that impacts of harvesting depend on the type and strength of density-dependent regulation. Here, we used logistic population growth models and an empirical reindeer case study to show that low to moderate harvesting can actually buffer populations against environmental perturbations. This occurs because of density-dependent environmental stochasticity, where negative environmental impacts on vital rates are amplified at high population density due to intraspecific resource competition. Simulations from our population models show that even low levels of harvesting may prevent overabundance, thereby dampening population fluctuations and reducing the risk of population collapse and quasi-extinction following environmental perturbations. Thus, depending on the species' life history and the strength of density-dependent environmental drivers, low to moderate harvesting can improve population resistance to increased climate variability and extreme weather expected under global warming.

The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal, and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable framework that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible framework “Movement-driven modeling of spatio-temporal infection risk” (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.

Seed dispersal directly affects plant establishment, gene flow, and fitness. As a result, understanding patterns in seed dispersal is fundamental to understanding plant ecology and evolution, as well as addressing challenges of extinction and global change. Our ability to understand dispersal is limited because few frameworks have emerged that provide a means for predicting dispersal across time and space. We provide a novel framework that links seed dispersal to animal social status, a key component of behavior. Because social status affects individual resource access and movement, it provides a critical link to two factors that determine seed dispersal: the quantity of seeds dispersed and the spatial patterns of dispersal. Moreover, individual social status may have unappreciated effects on post-dispersal seed survival and recruitment when social status affects individual habitat use. Hence, environmental changes, such as selective harvesting and urbanization, that affect animal social structure may have unappreciated consequences for seed dispersal. The framework we present highlights these exciting new hypotheses linking environmental change, social structure, and seed dispersal. By outlining experimental approaches to test these hypotheses, we hope to facilitate studies across a wide diversity of plant-frugivore networks, which may uncover emerging hotspots or catastrophic losses of seed dispersal.

While species interactions are fundamental for linking biodiversity to ecosystem functioning and for conservation, large-scale empirical data are lacking for most species and ecosystems. Accumulating evidence suggests that trophic interactions are predictable from available functional trait information, but we have yet to understand how well we can predict interactions across large spatial scales and food webs. Here, we built a model predicting predator-prey interactions based on functional traits for European vertebrates. We found that even models calibrated with very few known interactions (100 out of 71k) estimated the entire food web reasonably well. However, predators were easier to predict than prey, with prey in some clades being particularly difficult to predict (e.g., fowls and storks). Local food web connectance was also consistently over-estimated. Our results demonstrate the potential for filling gaps in sparse food webs, an important step towards a better description of biodiversity with strong implications for conservation planning.

Studies of populations oscillating through time have a long history in ecology as these dynamics can help provide insights into the causes of population regulation. A particularly difficult challenge is determining the relative role of deterministic versus stochastic forces in producing this oscillatory behavior. Another classic ecological study area is the study of spatial synchrony which also has helped unravel underlying population dynamic principles. One possible approach to understanding the causes of population cycles is based on the idea that a focus on spatiotemporal behavior, oscillations in coupled populations, can provide much further insight into the relative role of deterministic versus stochastic forces. Using ideas based on concepts from statistical physics, we develop results showing that in a system with coupling between adjacent populations, a study of spatial synchrony provides much information about the underlying causes of oscillations. Novel, to ecology, measures of spatial synchrony are a key step.