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Eelgrass creates critical coastal habitats worldwide and fulfills essential ecosystem functions as a foundation seagrass. Climate warming and disease threaten eelgrass, causing mass mortalities and cascading ecological impacts. Subtidal meadows are deeper than intertidal and may also provide refuge from the temperature-sensitive seagrass wasting disease. From cross-boundary surveys of 5761 eelgrass leaves from Alaska to Washington and assisted with a machine-language algorithm, we measured outbreak conditions. Across summers 2017 and 2018, disease prevalence was 16% lower for subtidal than intertidal leaves; in both tidal zones, disease risk was lower for plants in cooler conditions. Even in subtidal meadows, which are more environmentally stable and sheltered from temperature and other stressors common for intertidal eelgrass, we observed high disease levels, with half of the sites exceeding 50% prevalence. Models predicted reduced disease prevalence and severity under cooler conditions, confirming a strong interaction between disease and temperature. At both tidal zones, prevalence was lower in more dense eelgrass meadows, suggesting disease is suppressed in healthy, higher density meadows. These results underscore the value of subtidal eelgrass and meadows in cooler locations as refugia, indicate that cooling can suppress disease, and have implications for eelgrass conservation and management under future climate change scenarios. This article is part of the theme issue ‘Infectious disease ecology and evolution in a changing world’. 

Effective solutions to conserve biodiversity require accurate community‐ and species‐level information at relevant, actionable scales and across entire species' distributions. However, data and methodological constraints have limited our ability to provide such information in robust ways. Herein we employ a Deep‐Reasoning Network implementation of the Deep Multivariate Probit Model (DMVP‐DRNets), an end‐to‐end deep neural network framework, to exploit large observational and environmental data sets together and estimate landscape‐scale species diversity and composition at continental extents. We present results from a novel year‐round analysis of North American avifauna using data from over nine million eBird checklists and 72 environmental covariates. We highlight the utility of our information by identifying critical areas of high species diversity for a single group of conservation concern, the North American wood warblers, while capturing spatiotemporal variation in species' environmental associations and interspecific interactions. In so doing, we demonstrate the type of accurate, high‐resolution information on biodiversity that deep learning approaches such as DMVP‐DRNets can provide and that is needed to inform ecological research and conservation decision‐making at multiple scales.

 

Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

 

Seagrass meadows provide valuable ecosystem benefits but are at risk from disease. Eelgrass ( Zostera marina ) is a temperate species threatened by seagrass wasting disease (SWD), caused by the protist Labyrinthula zosterae . The pathogen is sensitive to warming ocean temperatures, prompting a need for greater understanding of the impacts on host health under climate change. Previous work demonstrates pathogen cultures grow faster under warmer laboratory conditions and documents positive correlations between warmer ocean temperatures and disease levels in nature. However, the consequences of disease outbreaks on eelgrass growth remain poorly understood. Here, we examined the effect of disease on eelgrass productivity in the field. We coupled in situ shoot marking with high-resolution imagery of eelgrass blades and used an artificial intelligence application to determine disease prevalence and severity from digital images. Comparisons of eelgrass growth and disease metrics showed that SWD impaired eelgrass growth and accumulation of non-structural carbon in the field. Blades with more severe disease had reduced growth rates, indicating that disease severity can limit plant growth. Disease severity and rhizome sugar content were also inversely related, suggesting that disease reduced belowground carbon accumulation. Finally, repeated measurements of diseased blades indicated that lesions can grow faster than healthy tissue in situ . This is the first study to demonstrate the negative impact of wasting disease on eelgrass health in a natural meadow. These results emphasize the importance of considering disease alongside other stressors to better predict the health and functioning of seagrass meadows in the Anthropocene. 

Computational advances reveal opportunities for more sustainable hydropower development in large transboundary river basins. 

Automated experimentation has yielded data acquisition rates that supersede human processing capabilities. Artificial Intelligence offers new possibilities for automating data interpretation to generate large, high-quality datasets. Background subtraction is a long-standing challenge, particularly in settings where multiple sources of the background signal coexist, and automatic extraction of signals of interest from measured signals accelerates data interpretation. Herein, we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest. The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals. While the model can incorporate prior knowledge, it does not require knowledge of the signals since the shapes of the background signals, the noise levels, and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework. Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets, a transformative capability with many applications in the physical sciences and beyond.