Ecology Letters

Lürig, Moritz D.; Di Martino, Emanuela; Porto, Arthur

doi: 10.1111/ele.14495pmid: 39136114

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.

Somveille, Marius; Bossu, Christen M.; DeSaix, Matthew G.; Alvarado, Allison H.; Gómez Villaverde, Sergio; Rodríguez Otero, Genaro; Hernández‐Baños, Blanca E.; Smith, Thomas B.; Ruegg, Kristen C.

doi: 10.1111/ele.14496pmid: 39132717

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.

Srinivasan, Umesh; Shanker, Kartik; Price, Trevor D.

doi: 10.1111/ele.14497pmid: 39169636

Using data on bird species elevational distributions from the world's mountain ranges, bird diets, and the distribution of the ant genus Oecophylla, we report that global patterns in bird elevational diversity show signals of competition with ants. Oecophylla is an abundant and effective predator of invertebrates, preying on the same species that invertivorous birds feed on. In mountain ranges with Oecophylla present in the foothills, the maximum species richness of invertivorous birds (but not other trophic guilds) occurs, on average, at 960 m, ca. 450 m higher than in mountain ranges without Oecophylla, resulting in a mid‐elevation peak in bird species richness. Where Oecophylla is absent, bird species richness for all guilds generally show monotonic declines with increasing elevation. We argue that Oecophylla reduces prey density for invertivorous birds and that low prey abundance reduces invertivorous bird density, which in turn is correlated with lower bird species richness. These findngs suggest that competition between distantly related taxa can set range limits, leading to emergent diversity patterns over large scales.

Gómez‐Llano, Miguel; Bassar, Ronald D.; Svensson, Erik I.; Tye, Simon P.; Siepielski, Adam M.

doi: 10.1111/ele.14477pmid: 39096013

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.

Fertakos, Matthew E.; Bradley, Bethany A.

doi: 10.1111/ele.14494pmid: 39136244

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.

Bonnaffé, Willem; Danet, Alain; Leclerc, Camille; Frossard, Victor; Edeline, Eric; Sentis, Arnaud

doi: 10.1111/ele.14480pmid: 39096032

Nutrient enrichment and climate warming threaten freshwater systems. Metabolic theory and the paradox of enrichment predict that both stressors independently can lead to simpler food‐webs having fewer nodes, shorter food‐chains and lower connectance, but cancel each other's effects when simultaneously present. Yet, these theoretical predictions remain untested in complex natural systems. We inferred the food‐web structure of 256 lakes and 373 streams from standardized fish community samplings in France. Contrary to theoretical predictions, we found that warming shortens fish food‐chain length and that this effect was magnified in enriched streams and lakes. Additionally, lakes experiencing enrichment exhibit lower connectance in their fish food‐webs. Our study suggests that warming and enrichment interact to magnify food‐web simplification in nature, raising further concerns about the fate of freshwater systems as climate change effects will dramatically increase in the coming decades.

Yoder, Jeremy B.; Andrade, Ana Karina; DeFalco, Lesley A.; Esque, Todd C.; Carlson, Colin J.; Shryock, Daniel F.; Yeager, Ray; Smith, Christopher I.

doi: 10.1111/ele.14478pmid: 39092581

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.

Ortiz‐Ross, Xochitl; Blumstein, Daniel T.

doi: 10.1111/ele.14485pmid: 39140409

Protecting populations contending with co‐occurring stressors requires a better understanding of how multiple early‐life stressors affect the fitness of natural systems. However, the complexity of such research has limited its advancement and prevented us from answering new questions. In human studies, cumulative risk models predict adult health risk based on early adversity exposure. We apply a similar framework in wild yellow‐bellied marmots (Marmota flaviventer). We tested cumulative adversity indices (CAIs) across different adversity types and time windows. All CAIs were associated with decreased pup survival and were well supported. Moderate and acute, but not standardized CAIs were associated with decreased lifespan, supporting the cumulative stress hypothesis and the endurance of early adversity. Multivariate models showed that differences in lifespan were driven by weaning date, precipitation, and maternal loss, but they performed poorly compared with CAI models. We highlight the development, utility, and insights of CAI approaches for ecology and conservation.

doi: 10.1111/ele.14257pmid: N/A

Lemos‐Costa, Paula; Miller, Zachary R.; Allesina, Stefano

doi: 10.1111/ele.14490pmid: 39152685

Species' traits and interactions are products of evolutionary history. Despite the long‐standing hypothesis that closely related species possess similar traits, and thus experience stronger competition, measuring the effect of evolutionary history on the ecology of natural communities remains challenging. We propose a novel framework to test whether phylogeny influences patterns of coexistence and abundance of species assemblages. In our approach, phylogenetic trees are used to parameterize species' interactions, which in turn determine the abundance of species in a given assemblage. We use likelihoods to score models parameterized with a given phylogeny, and contrast them with models built using random trees, allowing us to test whether phylogenetic information helps to predict species' abundances. Our statistical framework reveals that interactions are indeed structured by phylogeny in a large set of experimental plant communities. Our results confirm that evolutionary history can help predict, and potentially manage or conserve, the structure and function of complex ecological communities.