Trends in ecology & evolution最新文献

As biodiversity loss continues, targeted conservation interventions are increasingly necessary. Stemming species loss requires mechanistic understanding of the processes governing population dynamics. However, this information is unavailable for most animals because it requires data that are difficult to collect using traditional methods. Advances in animal tracking technology have generated an avalanche of high-resolution observations for a growing list of species around the globe. To date, most research using these data has focused on questions about animal behavior, with less emphasis on population processes. Here, we argue that tracking data are uniquely poised to bring powerful new insights to the urgent, global problem of halting species extinctions by revealing when, where, how, and why populations are changing.

Dollo's law of irreversibility argues that evolution cannot revert to earlier states. It has remained controversial ever since its inception in the 19th century. Enabled by advances in phylogenomics and functional genomics, recent studies show that there are very likely some cases of 'breaking Dollo's law'. As post-genomic research grows from showing patterns to revealing processes, new emphasis is needed on the molecular mechanisms by which Dollo's law might be broken. Shifting the argument from 'if it happened' to 'how it happened' will provide richer understanding of organismal and evolutionary biology. Motivated by case studies and novel avenues to test trait loss and regain, we outline a set of alternative hypotheses to be evaluated and what the outcomes tell us about evolution.

The consumption of ethanol has frequently been seen as largely restricted to humans. Here, we take a broad eco-evolutionary approach to understanding ethanol's potential impact on the natural world. There is growing evidence that ethanol is present in many wild fruits, saps, and nectars and that ethanol ingestion offers benefits that favour adaptations for its use in multiple taxa. Explanations for ethanol consumption span both the nutritional and non-nutritional, with potential medicinal value or cognitive effects (with social-behavioural benefits) explored. We conclude that ethanol is ecologically relevant and that it has shaped the evolution of many species and structured symbiotic relationships among organisms, including plants, yeast, bacteria, insects, and mammals.

Coastal ecosystems face salinization and rising temperatures. In coastal ectotherms, salinity and temperature affect metabolism, tolerance, infections, growth, behavior, and survival. Overall, the combined effects of salinity and temperature on species distribution, community structure, invasive species, and ecosystem functioning need to be fully assessed to understand impacts from these stressors.

Natural debris deposited by the sea is essential for the functioning of the beach ecosystem. As tourist demands on the coast grow, aesthetic values become more important, and the indiscriminate cleaning of debris spreads from urban to natural beaches. A change in beach debris management is needed to ensure that organic debris plays its role where the sea has deposited it.

The movement, distribution, and relative proportions of essential elements across the landscape should influence the structure and functioning of biological communities. Yet, our basic understanding of the spatial distribution of elements, particularly bioavailable elements, across landscapes is limited. Here, we propose a quantitative framework to study the causes and consequences of spatial patterns of elements. Specifically, we integrate distribution models, dissimilarity metrics, and spatial smoothing to predict how the distribution of bioavailable elements changes with spatial extent. Our community and landscape ecology perspective on elemental diversity highlights the characteristic relationships that emerge among elements in landscapes and that can be measured empirically to help us pinpoint ecosystem control points. This step forward provides a mechanistic link between community and ecosystem processes.

Language connects cultural and biological diversity and can contribute to both big data and localised approaches to improve conservation. Analysing Indigenous languages at regional level supports understanding of local ecologies and cultural revitalisation. Collated linguistic datasets can help to identify large-scale patterns, including extinctions, and forge robust multidisciplinary approaches to biocultural decision-making.

Soil microbial communities play pivotal roles in maintaining soil health in agroecosystems. However, how the delivery of multiple microbial functions in agroecosystems is maintained remains poorly understood. This may put us at risk of incurring unexpected trade-offs between soil functions. We elucidate how interactions between soil microbes can lead to trade-offs in the functioning of agricultural soils. Interactions within soil microbial communities can result in not only positive but also neutral and negative relationships among soil functions. Altering soil conditions through soil health-improving agricultural management can alleviate these functional trade-offs by promoting the diversity and interrelationships of soil microbes, which can help to achieve more productive and sustainable agroecosystems.

Environmental warming is thought to alter food web stability and functioning, but whether warming reduces food web resistance and resilience to further climatic events remains surprisingly unexplored. Warming experiments that superimpose acute disturbances are urgently needed to understand how extreme events further threaten the stability and multifunctionality of ecological networks.

Recent advances in bioacoustics combined with acoustic individual identification (AIID) could open frontiers for ecological and evolutionary research because traditional methods of identifying individuals are invasive, expensive, labor-intensive, and potentially biased. Despite overwhelming evidence that most taxa have individual acoustic signatures, the application of AIID remains challenging and uncommon. Furthermore, the methods most commonly used for AIID are not compatible with many potential AIID applications. Deep learning in adjacent disciplines suggests opportunities to advance AIID, but such progress is limited by training data. We suggest that broadscale implementation of AIID is achievable, but researchers should prioritize methods that maximize the potential applications of AIID, and develop case studies with easy taxa at smaller spatiotemporal scales before progressing to more difficult scenarios.