Ecology Letters最新文献

Non-native plants are often seen as peripheral to trophic networks due to a lack of co-evolution with local biota, but the factors shaping their integration remain poorly understood. Using a continent-wide dataset of 127,000 plant–microherbivore interactions across Europe, we show that native plants host more microherbivore species than non-natives. Among non-native plants, the number of associated microherbivore species was better predicted by time since introduction and range size in the introduced range than by relatedness to native flora or geographic origin. Species introduced more than two centuries ago, or with ranges as large as the average native plant, supported similar numbers of microherbivores as natives, though with a greater share of generalists. Our findings suggest that trophic networks can absorb novelty rapidly, with non-natives attaining levels of microherbivory richness comparable to natives over relatively short timescales, but the persistence of specialised interactions remains dependent on native flora.

Modelling has become a routine part of ecological and evolutionary research, yet its practice often lacks a clear conceptual framework. I propose that modelling can be fruitfully understood as experimentation. Like empirical studies, modelling projects involve treatments, levels and responses: parameter regimes or data manipulations serve as treatments, replicated runs yield summaries and comparisons across conditions reveal main effects and interactions. This framing sharpens design, reduces mission creep and clarifies communication. I outline common layers of abstraction, highlight good modelling habits and argue that thinking like an experimentalist fosters rigour, reliability and credibility without constraining creativity.

Biodiversity change forecasts rely on long-term time series, but such data are often scarce in space and time. Here, we interpolated spatiotemporal changes in species richness using a new method based on machine learning that does not require temporal replication at sites. Using 698,692 one-time sampled vegetation plots, we estimated trends in vascular plant alpha diversity across Europe and validated our approach against 22,852 independent time series. We found an overall near-zero net change in species richness between 1960 and 2020. However, species richness generally declined from 1960 to 1980 and increased from 2000 to 2020 across habitats. Declines were most pronounced in forests, but trends varied across habitats and regions, with overall increases at higher latitudes and elevations, and declines or stable trends elsewhere. Our findings demonstrate how data without temporal replication can be used to reveal context-dependent biodiversity dynamics, underscoring their importance for conservation and management.

The idea that natural systems tend to be at equilibrium dates back to the origin of the field of ecology and continues to underlie most ecological theory. However, empirical evidence for equilibrium dynamics in nature and in experiments is surprisingly elusive. Here, we address this conundrum by first exploring the history of equilibrium in ecological theory and the evidence for equilibrium dynamics in natural systems. We then search the literature to quantify how empiricists deal with equilibrium in their research and address barriers to integrating the concept of equilibrium into empirical work by providing step-by-step instructions for determining whether a population is at equilibrium. Next, we lay out three ways that equilibrium is embedded in theory, and for each, outline when meeting the equilibrium assumption in empirical tests is critical for scientific inference, and when it may be possible to relax this assumption. And finally, we present concrete steps that empiricists and theoreticians can each take in order to meet in the middle when it comes to equilibrium. We hope that this paper will stimulate new discussions from researchers from across the theory-empirical divide about this longstanding issue.

Drylands, supporting significant global biodiversity, exhibit abrupt, non-linear responses to increasing aridity with drastic declines after threshold crossing. While plant and soil responses are documented, impacts on other organisms remain unclear, as well as their potential interactions with other anthropogenic drivers. We investigated changes in taxonomic and trophic richness of multiple organisms, from bacteria to mammals, in response to aridity across 290 dryland ecoregions globally. All groups showed sequential threshold responses to aridity with threshold values ranging from 0.45 to 0.95 aridity levels, resulting in varying biodiversity losses (19%–54.3% depending on trophic group) after crossing such thresholds. Responses were most widespread in hyper-arid and arid regions, primarily affecting herbivores and detritivores in semi-arid areas, and were exacerbated by human disturbance and land-use change. However, primary productivity and richness of primary producers and prior trophic levels partially buffered these declines. Biodiversity conservation and reducing anthropogenic pressures can mitigate these losses.

Ecosystem states are often influenced by both concurrent and antecedent environmental drivers. However, the relative importance of antecedent conditions varies within and among ecosystems. Here, we analysed long-term depth-profile data from 382 temperate lakes across 10 countries to assess how differential changes in spring versus summer air temperature mediate summer water quality. We found that summer bottom-water conditions were more associated with spring air temperatures, while surface-water conditions were more associated with summer air temperatures. The relative influence of spring versus summer air temperature was mediated by lake morphometry, stratification and latitude. Across these lakes, summer air temperatures have increased more rapidly than spring air temperatures, potentially contributing to a growing thermal difference between surface and bottom waters (median = +0.5°C/decade). Consequently, our results demonstrate that predicting the ecological impacts of climate change may require considering spatial differences in ecological memory within ecosystems.

Woodstock and Harris raise concerns about generalisations in our paper ‘Calibrated Ecosystem Models Cannot Predict the Consequences of Conservation Management Decisions’. They implied that our conclusions are invalidated by the exclusion of density dependent compensatory feedback and the existence of automated calibration approaches with overfitting penalties—claims that overlook the broader structural issues we identified. Here, we explore their key criticism in more detail, clarify our position and explain why we continue to stand by our findings.