Ecology Letters最新文献

Invasions are commonly found to benefit from disturbance events. However, the importance of the relative timing of the invasion and disturbance for invader success and impact on community composition remains uncertain. Here, we experimentally test this by invading a five-species bacterial community on eight separate occasions—four before a disturbance and four after. Invader success and impact on community composition was greatest when the invasion immediately followed the disturbance. However, the subsequent invasions had negligible success or impact. Pre-disturbance, invader success and impact was greatest when the invader was added just before the disturbance. Importantly, however, the first three pre-disturbance invasion events had significantly greater success than the last three post-disturbance invasions. Moreover, these findings were consistent across a range of propagule pressures. Overall, we demonstrate that timing is highly important for both the success and impact on community composition of an invader, with both being lower as time since disturbance progresses.

Introduction history, including propagule pressure and residence time, has been proposed as a primary driver of biological invasions. However, it is unclear whether introduction history increases the likelihood that a species will be invasive or only the likelihood that it will be established. Using a dataset of non-native species historically available as ornamental plants in the conterminous United States, we investigated how introduction history relates to these stages of invasion. Introduction history was highly significant and a strong predictor of establishment, but only marginally significant and a poor predictor of invasive success. Propagule pressure predicted establishment better than residence time, with species likely to be established if they were introduced to only eight locations. These findings suggest that ongoing plant introductions will lead to widespread establishment but may not directly increase invasive success. Instead, other characteristics, like plant traits and local scale processes, may better predict whether a species becomes invasive.

A rapidly warming climate is driving changes in biodiversity worldwide, and its impact on insect communities is critical given their outsized role in ecosystem function and services. We use a long-term dataset of North American bumble bee species occurrences to determine whether the community temperature index (CTI), a measure of the balance of warm- and cool-adapted species in a community, has increased given warming temperatures. CTI has increased by an average of 0.99°C in strong association with warming maximum summer temperatures over the last 30 years with the areas exhibiting the largest increases including mid- to high latitudes as well as low and high elevations—areas relatively shielded from other intensive global changes. CTI shifts have been driven by the decline of cold-adapted species and increases in warm-adapted species within bumble bee communities. Our results show the pervasive impacts and ecological implications warming temperatures pose to insects.

In the realm of biological image analysis, deep learning (DL) has become a core toolkit, for example for segmentation and classification. However, conventional DL methods are challenged by large biodiversity datasets characterized by unbalanced classes and hard-to-distinguish phenotypic differences between them. Here we present BioEncoder, a user-friendly toolkit for metric learning, which overcomes these challenges by focussing on learning relationships between individual data points rather than on the separability of classes. BioEncoder is released as a Python package, created for ease of use and flexibility across diverse datasets. It features taxon-agnostic data loaders, custom augmentation options, and simple hyperparameter adjustments through text-based configuration files. The toolkit's significance lies in its potential to unlock new research avenues in biological image analysis while democratizing access to advanced deep metric learning techniques. BioEncoder focuses on the urgent need for toolkits bridging the gap between complex DL pipelines and practical applications in biological research.

Tracking climatic conditions throughout the year is often assumed to be an adaptive behaviour underlying seasonal migration patterns in animal populations. We investigate this hypothesis using genetic markers data to map migratory connectivity for 27 genetically distinct bird populations from 7 species. We found that the variation in seasonal climate tracking across our suite of populations at a continental scale is more likely a consequence, rather than a direct driver, of migratory connectivity, which is primarily shaped by energy efficiency—i.e., optimizing the balance between accessing available resources and movement costs. However, our results also suggest that regional-scale seasonal precipitation tracking affects population migration destinations, thus revealing a potential scale dependency of ecological processes driving migration. Our results have implications for the conservation of these migratory species under climate change, as populations tracking climate seasonally are potentially at higher risk if they adapt to a narrow range of climatic conditions.

Animals interact with nutrient cycles by consuming and depositing nutrients, interactions studied separately in nutritional ecology and zoogeochemistry. Recent theoretical work bridges these disciplines, highlighting that animal-driven nutrient recycling could be crucial in helping animals meet their nutritional needs. When animals exhibit site fidelity, they consistently deposit nutrients, potentially improving vegetation quality. We investigated this potential feedback by analysing changes in forage nitrogen stocks following simulated caribou calving. We found that forage nitrogen stocks increased after 2 weeks and remained elevated after 1 year, a change due to increased forage quality, not quantity. We also developed a nutrient budget within calving grounds, demonstrating that natal fluid and calf carcasses contribute substantial nitrogen subsidies. We, thus, highlight a positive zoogeochemical feedback whereby nutrients deposited during calving become bioavailable during lactation and provide evidence that site fidelity creates a biogeochemical boomerang in which animals deposit nutrients that can be reused later.

The Arctic is warming four times faster than the rest of the world, threatening the persistence of many Arctic species. It is uncertain if Arctic wildlife will have sufficient time to adapt to such rapidly warming environments. We used genetic forecasting to measure the risk of maladaptation to warming temperatures and sea ice loss in polar bears (Ursus maritimus) sampled across the Canadian Arctic. We found evidence for local adaptation to sea ice conditions and temperature. Forecasting of genome-environment mismatches for predicted climate scenarios suggested that polar bears in the Canadian high Arctic had the greatest risk of becoming maladapted to climate warming. While Canadian high Arctic bears may be the most likely to become maladapted, all polar bears face potentially negative outcomes to climate change. Given the importance of the sea ice habitat to polar bears, we expect that maladaptation to future warming is already widespread across Canada.

Explaining the maintenance of genetic variation in fitness-related traits within populations is a fundamental challenge in ecology and evolutionary biology. Frequency-dependent selection (FDS) is one mechanism that can maintain such variation, especially when selection favours rare variants (negative FDS). However, our general knowledge about the occurrence of FDS, its strength and direction remain fragmented, limiting general inferences about this important evolutionary process. We systematically reviewed the published literature on FDS and assembled a database of 747 effect sizes from 101 studies to analyse the occurrence, strength, and direction of FDS, and the factors that could explain heterogeneity in FDS. Using a meta-analysis, we found that overall, FDS is more commonly negative, although not significantly when accounting for phylogeny. An analysis of absolute values of effect sizes, however, revealed the widespread occurrence of modest FDS. However, negative FDS was only significant in laboratory experiments and non-significant in mesocosms and field-based studies. Moreover, negative FDS was stronger in studies measuring fecundity and involving resource competition over studies using other fitness components or focused on other ecological interactions. Our study unveils key general patterns of FDS and points in future promising research directions that can help us understand a long-standing fundamental problem in evolutionary biology and its consequences for demography and ecological dynamics.

Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (Yucca brevifolia and Y. jaegeriana), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo-referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records and reveals a slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years—but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity.

A significant fraction of Earth's ecosystems undergoes periodic wet-dry alternating transitional states. These globally distributed water-driven transitional ecosystems, such as intermittent rivers and coastal shorelines, have traditionally been studied as two distinct entities, whereas they constitute a single, interconnected meta-ecosystem. This has resulted in a poor conceptual and empirical understanding of water-driven transitional ecosystems. Here, we develop a conceptual framework that places the temporal availability of water as the core driver of biodiversity and functional patterns of transitional ecosystems at the global scale. Biological covers (e.g., aquatic biofilms and biocrusts) serve as an excellent model system thriving in both aquatic and terrestrial states, where their succession underscores the intricate interplay between these two states. The duration, frequency, and rate of change of wet-dry cycles impose distinct plausible scenarios where different types of biological covers can occur depending on their desiccation/hydration resistance traits. This implies that the distinct eco-evolutionary potential of biological covers, represented by their trait profiles, would support different functions while maintaining similar multifunctionality levels. By embracing multiple alternating transitional states as interconnected entities, our approach can help to better understand and manage global change impacts on biodiversity and multifunctionality in water-driven transitional ecosystems, while providing new avenues for interdisciplinary studies.