Anthropogenic activities have altered historical disturbance regimes, and understanding the mechanisms by which these shifting perturbations interact is essential to predicting where they may erode ecosystem resilience. Emerging infectious plant diseases, caused by human translocation of nonnative pathogens, can generate ecologically damaging forms of novel biotic disturbance. Further, abiotic disturbances, such as wildfire, may influence the severity and extent of disease‐related perturbations via their effects on the occurrence of hosts, pathogens and microclimates; however, these interactions have rarely been examined. The disease ‘sudden oak death’ (SOD), associated with the introduced pathogen Past wildfire altered disease dynamics and reduced SOD‐related mortality, indicating a negative interaction between these abiotic and biotic disturbances. Frequently burned forests were less likely to be invaded by
Information about the spatial distribution of species lies at the heart of many important questions in ecology. Logistical limitations and collection biases, however, limit the availability of such data at ecologically relevant scales. Remotely sensed information can alleviate some of these concerns, but presents challenges associated with accurate species identification and limited availability of field data for validation, especially in high diversity ecosystems such as tropical forests. Recent advances in machine learning offer a promising and cost‐efficient approach for gathering a large amount of species distribution data from aerial photographs. Here, we propose a novel machine learning framework, artificial perceptual learning (APL), to tackle the problem of weakly supervised pixel‐level mapping of tree species in forests. Challenges arise from limited availability of ground labels for tree species, lack of precise segmentation of tree canopies and misalignment between visible canopies in the aerial images and stem locations associated with ground labels. The proposed APL framework addresses these challenges by constructing a workflow using state‐of‐the‐art machine learning algorithms. We develop and illustrate the proposed framework by implementing a fine‐grain mapping of three species, the palm Receiver operating characteristic (ROC) curves and Intersection over Union (IoU) metrics demonstrate that APL substantially outperforms the basic workflow and attains human‐level cognitive economy, with 50‐fold time savings. For the palm and
- Award ID(s):
- 1831952
- NSF-PAR ID:
- 10452933
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 12
- Issue:
- 4
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- p. 608-618
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Phytophthora ramorum , causes acute, landscape‐scale tree mortality in California's fire‐prone coastal forests. Here, we examined interactions between wildfire and the biotic disturbance impacts of this emerging infectious disease. Leveraging long‐term datasets that describe wildfire occurrence andP. ramorum dynamics across the Big Sur region, we modelled the influence of recent and historical fires on epidemiological parameters, including pathogen presence, infestation intensity, reinvasion, and host mortality.P. ramorum , had lower incidence of host infection, and exhibited decreased disease‐related biotic disturbance, which was associated with reduced occurrence and density of epidemiologically significant hosts. Following a recent wildfire, survival of mature bay laurel, a key sporulating host, was the primary driver ofP. ramorum infestation and reinvasion, but younger, rapidly regenerating host vegetation capable of sporulation did not measurably influence disease dynamics. Notably, the effect ofP. ramorum infection on host mortality was reduced in recently burned areas, indicating that the loss of tall, mature host canopies may temporarily dampen pathogen transmission and ‘release’ susceptible species from significant inoculum pressure.Synthesis . Cumulatively, our findings indicate that fire history has contributed to heterogeneous patterns of biotic disturbance and disease‐related decline across this landscape, via changes to the both the occurrence of available hosts and the demography of epidemiologically important host populations. These results highlight that human‐altered abiotic disturbances may play a foundational role in structuring infectious disease dynamics, contributing to future outbreak emergence and driving biotic disturbance regimes. -
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Summary Trade‐offs among carbon sinks constrain how trees physiologically, ecologically, and evolutionarily respond to their environments. These trade‐offs typically fall along a productive growth to conservative, bet‐hedging continuum. How nonstructural carbohydrates (NSCs) stored in living tree cells (known as carbon stores) fit in this trade‐off framework is not well understood.
We examined relationships between growth and storage using both within species genetic variation from a common garden, and across species phenotypic variation from a global database.
We demonstrate that storage is actively accumulated, as part of a conservative, bet‐hedging life history strategy. Storage accumulates at the expense of growth both within and across species. Within the species
Populus trichocarpa , genetic trade‐offs show that for each additional unit of wood area growth (in cm2 yr−1) that genotypes invest in, they lose 1.2 to 1.7 units (mg g−1NSC) of storage. Across species, for each additional unit of area growth (in cm2 yr−1), trees, on average, reduce their storage by 9.5% in stems and 10.4% in roots.Our findings impact our understanding of basic plant biology, fit storage into a widely used growth‐survival trade‐off spectrum describing life history strategy, and challenges the assumptions of passive storage made in ecosystem models today.
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Abstract Understanding the mechanisms that promote the coexistence of hundreds of species over small areas in tropical forest remains a challenge. Many tropical tree species are presumed to be functionally equivalent shade tolerant species but exist on a continuum of performance trade‐offs between survival in shade and the ability to quickly grow in sunlight. These trade‐offs can promote coexistence by reducing fitness differences.
Variation in plant functional traits related to resource acquisition is thought to predict variation in performance among species, perhaps explaining community assembly across habitats with gradients in resource availability. Many studies have found low predictive power, however, when linking trait measurements to species demographic rates.
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We combined long‐term studies of seedling dynamics with functional trait data collected at a standard life‐history stage in three diverse neotropical forests to ask whether variation in coordinated suites of traits predicts variation among species in demographic performance.
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Abstract Background Lung cancer is the deadliest and second most common cancer in the United States due to the lack of symptoms for early diagnosis. Pulmonary nodules are small abnormal regions that can be potentially correlated to the occurrence of lung cancer. Early detection of these nodules is critical because it can significantly improve the patient's survival rates. Thoracic thin‐sliced computed tomography (CT) scanning has emerged as a widely used method for diagnosing and prognosis lung abnormalities.
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Results Using only 10, 30, 50, and 100 training samples sequentially, our method constantly improves the classification performance and explanation quality of baseline in terms of Area Under the Curve (AUC) and Intersection over Union (IoU). In particular, our framework with a learnable imputation kernel improves IoU from baseline by 24.0% to 80.0%. A pre‐defined Gaussian imputation kernel achieves an even greater improvement, from 38.4% to 118.8% from baseline. Compared to the baseline trained on 100 samples, our method shows less drop in AUC when trained on fewer samples. A comprehensive comparison of interpretability shows that our method aligns better with expert opinions.
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