Ecology Letters最新文献

Ecology often seeks to answer causal questions, and while ecologists have a rich history of experimental approaches, novel observational data streams and the need to apply insights across naturally occurring conditions pose opportunities and challenges. Other fields have developed causal inference approaches that can enhance and expand our ability to answer ecological causal questions using observational or experimental data. However, the lack of comprehensive resources applying causal inference to ecological settings and jargon from multiple disciplines creates barriers. We introduce approaches for causal inference, discussing the main frameworks for counterfactual causal inference, how causal inference differs from other research aims and key challenges; the application of causal inference in experimental and quasi-experimental study designs; appropriate interpretation of the results of causal inference approaches given their assumptions and biases; foundational papers; and the data requirements and trade-offs between internal and external validity posed by different designs. We highlight that these designs generally prioritise internal validity over generalisability. Finally, we identify opportunities and considerations for ecologists to further integrate causal inference with synthesis science and meta-analysis and expand the spatiotemporal scales at which causal inference is possible. We advocate for ecology as a field to collectively define best practices for causal inference.

Previous studies have primarily focused on single abrupt shifts; however, the actual ecosystem will experience continuous abrupt shifts (CAS), including different directions shifts (DDS) and same direction shifts (SDS). The patterns and drivers of these CAS remain unclear. We examined the patterns of the DDS and SDS by two vegetation datasets and then tested climate drivers comprising atmospheric temperature (MAT), atmospheric precipitation (MAP), soil temperature (ST) and soil water content (SW); finally, hysteresis effects were examined with reference to principal drivers. The results demonstrate that the DDS and SDS varied across climatic regions. The ST, SW, MAT and MAP were the primary drivers of the DDS, while the MAT and MAP were the primary drivers of the SDS. Furthermore, the presence of hysteresis effects was validated via the DDS. This study presents the widespread occurrence of the CAS and the divergent roles of climate change on the DDS and SDS globally.

Predicting the effects of climate change on plant disease is critical for protecting ecosystems and food production. Here, we show how disease pressure responds to short-term weather, historical climate and weather anomalies by compiling a global database (4339 plant–disease populations) of disease prevalence in both agricultural and wild plant systems. We hypothesised that weather and climate would play a larger role in disease in wild versus agricultural plant populations, which the results supported. In wild systems, disease prevalence peaked when the temperature was 2.7°C warmer than the historical average for the same time of year. We also found evidence of a negative interactive effect between weather anomalies and climate in wild systems, consistent with the idea that climate maladaptation can be an important driver of disease outbreaks. Temperature and precipitation had relatively little explanatory power in agricultural systems, though we observed a significant positive effect of current temperature. These results indicate that disease pressure in wild plants is sensitive to nonlinear effects of weather, weather anomalies and their interaction with historical climate. In contrast, warmer temperatures drove risks for agricultural plant disease outbreaks within the temperature range examined regardless of historical climate, suggesting vulnerability to ongoing climate change.

Quantifying how co-acting global change factors (GCFs) influence plant invasion is crucial for predicting future invasion dynamics. We did a meta-analysis to assess pairwise effects of five GCFs (elevated CO₂, drought, eutrophication, increased rainfall and warming) on native and alien plants. We found that alien plants, compared to native plants, suffered less or benefited more for four of the eight pairwise GCF combinations, and that all GCFs acted additively. Subgroup analysis showed that the relative benefits of alien over native plants were particularly apparent when they grew in competition with one another, and that the results were largely the same when the aliens were restricted to naturalised or invasive species. Our meta-analysis provides evidence that additive effects of multiple global change factors on plant invasions are common, and thus that with the ongoing global environmental changes, the risk of plant invasion continues to increase.

Accurately representing the relationships between nitrogen supply and photosynthesis is crucial for reliably predicting carbon–nitrogen cycle coupling in Earth System Models (ESMs). Most ESMs assume positive correlations amongst soil nitrogen supply, leaf nitrogen content, and photosynthetic capacity. However, leaf photosynthetic nitrogen demand may influence the leaf nitrogen response to soil nitrogen supply; thus, responses to nitrogen supply are expected to be the largest in environments where demand is the greatest. Using a nutrient addition experiment replicated across 26 sites spanning four continents, we demonstrated that climate variables were stronger predictors of leaf nitrogen content than soil nutrient supply. Leaf nitrogen increased more strongly with soil nitrogen supply in regions with the highest theoretical leaf nitrogen demand, increasing more in colder and drier environments than warmer and wetter environments. Thus, leaf nitrogen responses to nitrogen supply are primarily influenced by climatic gradients in photosynthetic nitrogen demand, an insight that could improve ESM predictions.

Microbial traits are critical for carbon sequestration and degradation in terrestrial ecosystems. Yet, our understanding of the relationship between carbon metabolic strategies and genomic traits like genome size remains limited. To address this knowledge gap, we conducted a global-scale meta-analysis of 2650 genomes, integrated whole-genome sequencing data, and performed a continental-scale metagenomic field study. We found that genome size was tightly associated with an increase in the ratio between genes encoding for polysaccharide decomposition and biomass synthesis that we defined as the carbon acquisition-to-biomass yield ratio (A/Y). We also show that horizontal gene transfer played a major evolutionary role in the expanded bacterial capacities in carbon acquisition. Our continental-scale field study further revealed a significantly negative relationship between the A/Y ratio and soil organic carbon stocks. Our work demonstrates a global relationship between genome size and the encoded carbon metabolic strategies of soil bacteria across terrestrial microbiomes.

The geographic mosaic of coevolution predicts reciprocal selection, the first step in coevolution, to vary with changing biotic and abiotic environmental conditions. Studying how temperature affects reciprocal selection is essential to connect effects of global warming on the microevolutionary patterns of coevolution to the ecological processes underlying them. In this study, we investigated whether temperature influenced reciprocal selection between a plant (Brassica rapa) and its pollinating butterfly herbivore (Pieris rapae). In two temperature environments (ambient and hot), we measured the phenotypes of plants and butterflies, their interactions and fitness, which we used to calculate reciprocal selection. We found a variety of traits involved in reciprocal selection in the ambient environment, but none in the hot environment. We provide experimental evidence that elevated temperature weakens reciprocal selection, which will help better predict the consequences of global warming for coevolution.