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1. The Janzen-Connell (J-C) Model proposes that host-specific enemies maintain high tree species diversity by reducing seedling performance near conspecific adults and promoting replacement by heterospecific seedlings. Support for this model often comes from decreased performance for a species at near versus far distances from conspecific adults. However, the relative success of conspecific versus heterospecific seedlings recruiting under a given tree species is a critical, but untested, component of the J-C Model. 2. In a shade-house experiment, we tested plant-soil feedbacks as a J-C mechanism in six tropical tree species. We assessed effects of conspecific versus heterospecific cultured soil extracts on seedling performance for each species, and we compared performance of conspecific versus heterospecific seedlings grown with soil extract cultured by a particular tree species. Additionally, we tested whether soil microbes were creating these plant-soil feedbacks and whether low light increased species vulnerability to pathogens. 3. Among 30 potential comparisons of survival and mass for seedlings grown in conspecific versus heterospecific soil extracts, survival decreased in seven and increased in two, whereas mass decreased in 13 and increased in 1. To integrate survival and growth, we also examined seedling performance [(mean total mass x mean survival time)/(days of experiment)], which was lower in 16 and higher in 2 of 30 comparisons between seedlings grown with soil extract cultured by conspecific versus heterospecific individuals. Based on performance within a soil extract, conspecific seedlings were disadvantaged in 15 and favoured in 7 of 30 cases relative to heterospecific seedlings. 4. Species pairwise interactions of soil modification and seedling performance occurred regardless of sterilization, suggesting chemical mediation. Microbes lacked host-specificity and reduced performance regardless of extract source and irradiance. 5. Synthesis. These results, along with parallel research in temperate forests, suggest that plant-soil feedbacks are an important component of seedling dynamics in both ecosystems. However, negative conspecific feedbacks were more prevalent in tropical than temperate species. Thus, negative plant-soil feedbacks appear to facilitate species coexistence via negative distance-dependent processes in tropical but not temperate forests, but the feedbacks were mediated through chemical effects rather than through natural enemies as expected under the J-C Model.

In recent years, deep learning has been successfully applied to hyperspectral image classification (HSI) problems, with several convolutional neural network (CNN) based models achieving an appealing classification performance. However, due to the multi-band nature and the data redundancy of the hyperspectral data, the CNN model underperforms in such a continuous data domain. Thus, in this article, we propose an end-to-end transformer model entitled SAT Net that is appropriate for HSI classification and relies on the self-attention mechanism. The proposed model uses the spectral attention mechanism and the self-attention mechanism to extract the spectral–spatial features of the HSI image, respectively. Initially, the original HSI data are remapped into multiple vectors containing a series of planar 2D patches after passing through the spectral attention module. On each vector, we perform linear transformation compression to obtain the sequence vector length. During this process, we add the position–coding vector and the learnable–embedding vector to manage capturing the continuous spectrum relationship in the HSI at a long distance. Then, we employ several multiple multi-head self-attention modules to extract the image features and complete the proposed network with a residual network structure to solve the gradient dispersion and over-fitting problems. Finally, we employ a multilayer perceptron for the HSI classification. We evaluate SAT Net on three publicly available hyperspectral datasets and challenge our classification performance against five current classification methods employing several metrics, i.e., overall and average classification accuracy and Kappa coefficient. Our trials demonstrate that SAT Net attains a competitive classification highlighting that a Self-Attention Transformer network and is appealing for HSI classification.

Fifty years ago, Janzen (1970) and Connell (1971) independently published a revolutionary idea to explain the hyperdiverse tree communities of the tropics. The essential observations were that seedfall is concentrated in the vicinity of fruiting trees, whereas saplings recruit at a distance from reproductive conspecifics. These observations were encapsulated in a simple focal-tree model constructed of intersecting curves for seedfall and escape from host-specific enemies postulated to attack propagules (seeds and seedlings) in the vicinity of reproductive conspecifics. In conflict with the thinking of the times, the mechanism operates from the top down rather than from the bottom up. A deterrent to broad acceptance has been the giant intuitive leap required to generalize the focal tree model to an entire forest community. Recent theoretical and empirical results have succeeded in bridging the gap between the focal tree model and its community-level implications. With these new findings, Janzen–Connell has come of age.

A flexible approach for risk aggregation is considered. The model consists of a tree structure, bivariate copulas, and marginal distributions. The construction relies on a conditional independence assumption whose implications are studied. A procedure for selecting the tree structure is developed using hierarchical clustering techniques, along with a distance metric based on Kendall's tau. Estimation, simulation, and model validation are also discussed. The approach is illustrated using data from a Canadian property and casualty insurance company. Les auteurs considèrent une approche flexible pour l'agrégation de risques basée sur un modèle formé d'une arborescence, de copules bivariées et de lois marginales. La construction s'appuie sur un postulat d'indépendance conditionnelle dont ils étudient les ramifications. Ils montrent comment choisir l'arborescence au moyen de techniques de classification et d'une métrique définie à partir du tau de Kendall et abordent également l'estimation, la simulation et l'adéquation du modèle. Enfin, les auteurs illustrent leur méthode à l'aide de données d'une compagnie canadienne en assurance IARD.

Background tree mortality is a complex process that requires large sample sizes and long timescales to disentangle the suite of ecological factors that collectively contribute to tree stress, decline, and eventual mortality. Tree mortality associated with acute disturbance events, in contrast, is conspicuous and frequently studied, but there remains a lack of research regarding the role of background mortality processes in mediating the severity and delayed effects of disturbance. We conducted an empirical study by measuring the rates, causes, and spatial pattern of mortality annually among 32,989 individual trees within a large forest demography plot in the Sierra Nevada. We characterized the relationships between background mortality, compound disturbances (fire and drought), and forest spatial structure, and we integrated our findings with a synthesis of the existing literature from around the world to develop a conceptual framework describing the spatio‐temporal signatures of background and disturbance‐related tree mortality. The interactive effects of fire, drought, and background mortality processes altered the rate, spatial structuring, and ecological consequences of mortality. Before fire, spatially non‐random mortality was only evident among small (1 < cm DBH ≤ 10)‐ and medium (10 < cm DBH ≤ 60)‐diameter classes; mortality rates were low (1.7% per yr), and mortality was density‐dependent among small‐diameter trees. Direct fire damage caused the greatest number of moralities (70% of stems ≥1 cm DBH), but the more enduring effects of this disturbance on the demography and spatial pattern of large‐diameter trees occurred during the post‐fire mortality regime. The combined effects of disturbance and biotic mortality agents provoked density‐dependent mortality among large‐diameter (≥60 cm DBH) trees, eliciting a distinct post‐disturbance mortality regime that did not resemble the pattern of either pre‐fire mortality or direct fire effects. The disproportionate ecological significance of the largest trees renders this mortality regime acutely consequential to the long‐term structure and function of forests.

Soilborne pathogens can contribute to diversity maintenance in tree communities through the Janzen-Connell effect, whereby the pathogenic reduction of seedling performance attenuates with distance from conspecifics. By contrast, arbuscular mycorrhizal fungi (AMF) have been reported to promote seedling performance; however, it is unknown whether this is also distance dependent. Here, we investigate the distance dependence of seedling performance in the presence of both pathogens and AMF. In a subtropical forest in south China, we conducted a four-year field census of four species with relatively large phylogenetic distances and found no distance-dependent mortality for newly germinated seedlings. By experimentally separating the effects of AMF and pathogens on seedling performance of six subtropical tree species in a shade house, we found that soil pathogens significantly inhibited seedling survival and growth while AMF largely promoted seedling growth, and these effects were host specific and declined with increasing conspecific distance. Together, our field and experimental results suggest that AMF can neutralize the negative effect of pathogens and that the Janzen-Connell effect may play a less prominent role in explaining diversity of nondominant tree species than previously thought.

The high-frequency decay parameter (κ) is investigated using the three-component broadband seismograms from 306 earthquakes with ML 3.1–5.6 recorded at nine Iranian National Broadband Seismic Network (BIN) stations in the Alborz region and adjacent areas. The individual κ values are calculated for both the horizontal and vertical components of each record. The estimated mean horizontal and vertical κ values are 0.051 and 0.035 s, respectively, indicating slightly lower attenuation of high-frequency energy on the vertical component than the horizontal one. The dependence of the kappa values on path and source parameters such as distance, magnitude, and focal mechanism are also investigated. A clear increasing trend is observed for κ values with hypocentral distances for horizontal and vertical components. The zero-distance kappa (κ0) values for the nine BIN stations are evaluated, and a mean value of 0.013 s is estimated, which is close to the values expected for generic rock sites. The obtained κ values show no significant correlation with the earthquake size in the magnitude range of our events. Furthermore, the κ values are found to be fairly similar for all faulting types, with a slight decrease in κ for strike-slip events; hence, the kappa values are deemed as independent of faulting type.

In recent years, the deep learning-based hyperspectral image (HSI) classification method has achieved great success, and the convolutional neural network (CNN) method has achieved good classification performance in the HSI classification task. However, the convolutional operation only works with local neighborhoods, and is effective in extracting local features. It is difficult to capture interactive features over long distances, which affects the accuracy of classification to some extent. At the same time, the data from HSI have the characteristics of three-dimensionality, redundancy, and noise. To solve these problems, we propose a 3D self-attention multiscale feature fusion network (3DSA-MFN) that integrates 3D multi-head self-attention. 3DSA-MFN first uses different sized convolution kernels to extract multiscale features, samples the different granularities of the feature map, and effectively fuses the spatial and spectral features of the feature map. Then, we propose an improved 3D multi-head self-attention mechanism that provides local feature details for the self-attention branch, and fully exploits the context of the input matrix. To verify the performance of the proposed method, we compare it with six current methods on three public datasets. The experimental results show that the proposed 3DSA-MFN achieves competitive classification and highlights the HSI classification task.

Named entity recognition is a fundamental step in biomedical text mining tasks where discontinuous named entity recognition, shows more non-continuous and overlapping due to the long and numerous biomedical specialized vocabulary. Hence, the discontinuous named entity recognition task in biomedical text mining faces an enormous challenge. Current methods suffer from long-distance dependence and insufficient information interaction between mentions. In this paper, frequent words and syntactic context integrated for the biomedical discontinuous named entity recognition method is proposed. First, a distance-independent co-occurrence feature mining method is designed for solving the long-distance dependence issue by adopting fine-grained syntactic and long-distance co-occurring information to enrich the semantic feature of context. Second, a co-occurrence-guided multidimensional feature fusion method is devised to model triple which is the semantics, syntax, and frequent word of the contextual information for improving the text fusion performance. Empirical results on real-world biomedical datasets demonstrate that our model outperforms current benchmark models in precision, recall, and F1 value.

Volumetric brain reconstructions provide an unprecedented opportunity to gain insights into the complex connectivity patterns of neurons in an increasing number of organisms. Here, we model and quantify the complexity of the resulting neural connectomes in the fruit fly, mouse, and human and unveil a simple set of shared organizing principles across these organisms. To put the connectomes in a physical context, we also construct contactomes, the network of neurons in physical contact in each organism. With these, we establish that physical constraints—either given by pairwise distances or the contactome—play a crucial role in shaping the network structure. For example, neuron positions are highly optimal in terms of distance from their neighbors. Yet, spatial constraints alone cannot capture the network topology, including the broad degree distribution. Conversely, the degree sequence alone is insufficient to recover the spatial structure. We resolve this apparent mismatch by formulating scalable maximum entropy models, incorporating both types of constraints. The resulting generative models have predictive power beyond the input data, as they capture several additional biological and network characteristics, like synaptic weights and graphlet statistics. We investigate the interplay of the spatial and topological structure of millimeter-scale neural connectomes in fly, mouse, and human. As a spatial observation, we demonstrate that the probability of synaptic connection decays exponentially with distance. Additionally, we show that the wiring length in neural connectomes is highly optimal. To quantify the physical constraints on synapse formation, we construct the physical contact network for each organism and demonstrate that contact edge probability follows the same exponential functional form as the connectome. At the same time, we show that spatial constraints are necessary but not sufficient to reconstruct the connectome topology. We present maximum-entropy models capturing key spatial and topological aspects of the connectomes and demonstrate their predictive power beyond the input data.