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Aims: Patterns of spatial community dissimilarity have inspired a large body of theory in ecology and biogeography. Yet key gaps remain in our understanding of the local-scale ecological processes underlying species replacement and species nestedness, the two fundamental components of spatial community dissimilarity. Here, we examined the relative influence of dispersal limitation, habitat filtering and interspecific species interactions on local-scale patterns of the replacement and nestedness components in eight stem-mapped temperate forest mega-plots at different ontogenetic stages (large versus small trees). Location: Eight large (20–35 ha), fully mapped temperate forest plots in northern China and northern U.S.A. Time period: 2004–2016. Major taxa studied: Woody plants. Methods: We combined decomposition of community dissimilarity (based on the Ružička index) and spatial point-pattern analysis to compare the spatial (i.e., distance-dependent) replacement and nestedness components of each plot with that expected under five spatially explicit null models representing different hypotheses on community-assembly mechanisms. Results: Our analyses revealed complex results. In all eight forests, spatial community dissimilarity was best explained by species replacement among local tree assemblages and by a null model based on dispersal limitation. In contrast, spatial nestedness for large and small trees was best explained by random placement and habitat filtering, respectively, in addition to dispersal limitation. However, interspecific interactions did not contribute to local replacement and nestedness. Main conclusions: Species replacement is the predominant process accounting for spatial community dissimilarity in these temperate forests and caused largely by local-scale species clustering associated with dispersal limitation. Nestedness, in contrast, is less prevalent and primarily associated with larger variation in local species richness as caused by spatial richness gradients or 'hotspots' of local species richness. The novel use of replacement and nestedness measures in point pattern analysis is a promising approach to assess local-scale biodiversity patterns and to explore their causes.

Numerous studies have examined the neural substrates of intertemporal decision‐making, but few have systematically investigated separate neural representations of the two attributes of future rewards (i.e., the amount of the reward and the delay time). More importantly, no study has used the novel analytical method of representational connectivity analysis (RCA) to map the two dimensions' functional brain networks at the level of multivariate neural representations. This study independently manipulated the amount and delay time of rewards during an intertemporal decision task. Both univariate and multivariate pattern analyses showed that brain activity in the dorsomedial prefrontal cortex (DMPFC) and lateral frontal pole cortex (LFPC) was modulated by the amount of rewards, whereas brain activity in the DMPFC and dorsolateral prefrontal cortex (DLPFC) was modulated by the length of delay. Moreover, representational similarity analysis (RSA) revealed that even for the regions of the DMPFC that overlapped between the two dimensions, they manifested distinct neural activity patterns. In terms of individual differences, those with large delay discounting rates (k) showed greater DMPFC and LFPC activity as the amount of rewards increased but showed lower DMPFC and DLPFC activity as the delay time increased. Lastly, RCA suggested that the topological metrics (i.e., global and local efficiency) of the functional connectome subserving the delay time dimension inversely predicted individual discounting rate. These findings provide novel insights into neural representations of the two attributes in intertemporal decisions, and offer a new approach to construct task‐based functional brain networks whose topological properties are related to impulsivity. DMPFC represented both amount and delay‐time with distinct activation patterns. RCA‐based amount‐related and time‐related networks topological properties can predict k.

Several studies have examined the neural substrates of probabilistic decision‐making, but few have systematically investigated the neural representations of the two objective attributes of probabilistic rewards, that is, the reward amount and the probability. Specifically, whether there are common or distinct neural activity patterns to represent the objective attributes and their association with the neural representation of the subjective valuation remains largely underexplored. We conducted two studies ( n Study1  = 34, n Study2  = 41) to uncover distributed neural representations of the objective attributes and subjective value as well as their association with individual probability discounting rates. The amount and probability were independently manipulated to better capture brain signals sensitive to these two attributes and were presented simultaneously in Study 1 and successively in Study 2. Both univariate and multivariate pattern analyses showed that the brain activities in the superior parietal lobule (SPL), including the postcentral gyrus, were modulated by the amount of rewards and probability in both studies. Further, representational similarity analysis revealed a similar neural representation between these two objective attributes and between the attribute and valuation. Moreover, the SPL tracked the subjective value integrated by the hyperbolic function. Probability‐related brain activations in the inferior parietal lobule were associated with the variability in individual discounting rates. These findings provide novel insights into a similar neural representation of the two attributes during probabilistic decision‐making and perhaps support the common neural coding of stimulus objective properties and subjective value in the field of probabilistic discounting.

•Conclusions by bloodstain pattern analysts were often erroneous and often contradicted other analysts.•On samples with known causes, 11.2% of responses were erroneous.•Both semantic differences and contradictory interpretations contributed to errors and disagreements. Although the analysis of bloodstain pattern evidence left at crime scenes relies on the expert opinions of bloodstain pattern analysts, the accuracy and reproducibility of these conclusions have never been rigorously evaluated at a large scale. We investigated conclusions made by 75 practicing bloodstain pattern analysts on 192 bloodstain patterns selected to be broadly representative of operational casework, resulting in 33,005 responses to prompts and 1760 short text responses. Our results show that conclusions were often erroneous and often contradicted other analysts. On samples with known causes, 11.2% of responses were erroneous. The results show limited reproducibility of conclusions: 7.8% of responses contradicted other analysts. The disagreements with respect to the meaning and usage of BPA terminology and classifications suggest a need for improved standards. Both semantic differences and contradictory interpretations contributed to errors and disagreements, which could have serious implications if they occurred in casework.

Bloodstain Pattern Analysis (BPA) helps us understand how bloodstains form, with a focus on their size, shape, and distributions. This aids in crime scene reconstruction and provides insight into victim positions and crime investigation. One challenge in BPA is distinguishing between different types of bloodstains, such as those from firearms, impacts, or other mechanisms. Our study focuses on differentiating impact spatter bloodstain patterns from gunshot backward spatter bloodstain patterns. We distinguish patterns by extracting well-designed individual stain features, applying effective data consolidation methods, and selecting boosting classifiers. As a result, our model exhibits competitive accuracy and efficiency on the tested dataset, suggesting its potential in similar scenarios. •A novel method distinguishes gunshot and impact bloodstain patterns using images.•Ellipse- and shade-based features improve interpretability and classification accuracy.•XGBoost achieves 92.89% accuracy, outperforming previous BPA models.•A new Stability Importance Score offers consistent feature ranking across model runs.•The method shows strong potential for practical forensic bloodstain analysis.

Spatial processes underlie major species coexistence mechanisms. A range of spatial analysis techniques are increasingly applied to data of fully mapped communities to quantify spatial structures in species and phylogenetic and functional diversity at some given spatial scale with the goal of gaining insights into processes of community assembly and dynamics. We review these techniques, including spatial point pattern analysis, quadrat-based analyses, and individual-based neighborhood models, and provide a practical roadmap for ecologists in the analysis of local spatial structures in species and phylogenetic and functional diversity. We show how scale-dependent metrics of spatial diversity can be used in concert with ecological null models, statistical models, and dynamic community simulation models to detect spatial patterns, reveal the influence of the biotic neighborhood on plant performance, and quantify the relative contribution of species interactions, habitat heterogeneity, and stochastic processes to community assembly across scale. Future works should integrate these approaches into a dynamic spatiotemporal framework.

ContextForest restoration plays an important role in global efforts to slow biodiversity loss and mitigate climate change. Vegetation in remnant forests can form striking patterns that relate to ecological processes, but restoration targets tend to overlook spatial pattern. While observations of intact reference ecosystems can help to inform restoration targets, field surveys are ill-equipped to map and quantify spatial pattern at a range of scales, and new approaches are needed.ObjectiveThis review sought to explore practical options for creating landscape-scale forest restoration targets that embrace spatial pattern.MethodsWe assessed how hierarchy theory, satellite remote sensing, landscape pattern analysis, drone-based remote sensing and spatial point pattern analysis could be applied to assess the spatial pattern of reference landscapes and inform forest restoration targets.ResultsHierarchy theory provides an intuitive framework for stratifying landscapes as nested hierarchies of sub-catchments, forest patches and stands of trees. Several publicly available tools can map patches within landscapes, and landscape pattern analysis can be applied to quantify the spatial pattern of these patches. Drones can collect point clouds and orthomosaics at the stand scale, a plethora of software can create maps of individual trees, and spatial point pattern analysis can be applied to quantify the spatial pattern of mapped trees.ConclusionsThis review explored several practical options for producing landscape scale forest restoration targets that embrace spatial pattern. With the decade on ecosystem restoration underway, there is a pressing need to refine and operationalise these ideas.

Within communities, organisms potentially self-organize through endogenous processes that create nonrandom spatial structure as they interact with one another or modify the abiotic environment. In contrast, exogenous processes such as environmental heterogeneity or variable immigration are thought to be dominant processes controlling these spatial patterns. Although both endogenous and exogenous processes likely occur, their relative importance is still largely unknown because of limited analytical tools and the lack of experimental evidence, particularly those that address exogenous sources of environmental heterogeneity. Here, we used a soil heterogeneity experiment to examine the relative effect of endogenous and exogenous processes on plant spatial structure after five years of community assembly. Soil heterogeneity was manipulated by splitting the vertical soil profile into three soil-types that were randomly assigned to 40 × 40 cm patches within 2.4 × 2.4 m plots. Homogeneous plots were created by mixing all soils before filling each patch. Thirty-four grassland species were then sown into all plots and allowed to grow for five years after which the location of all plants was mapped using a 5 × 5 cm grid. Results from point-pattern spatial analysis indicated that, even in the absence of soil heterogeneity and with initial seed arrival, spatial structure was primarily generated by endogenous processes. Although soil heterogeneity increased species aggregation at certain scales, most of the spatial structure was created by endogenous processes. These results suggest that endogenous processes may be more important than expected for generating spatial structure and can develop much faster than anticipated.

In criminal investigations, distinguishing between impact spatters and fly spots presents a challenge due to their morphological similarities. Traditional methods of bloodstain pattern analysis (BPA) rely significantly on the expertise of professional examiners, which can result in limitations including low identification efficiency, high misjudgment rates, and susceptibility to external disturbances. To enhance the accuracy and scientific rigor of identifying impact spatters and fly spots, this study employed artificial intelligence techniques in image recognition and transfer learning. Two types of bloodstains obtained from simulation experiments were utilized as datasets, and a pre-trained neural network, ResNet-18, was employed for feature extraction. The original fully connected layer was replaced, and a new fully connected layer with a dimensionality of 2 was introduced to fulfil the task requirements. The results demonstrate that the transfer learning network model, based on ResNet-18, achieved a maximum accuracy of 93 % in morphologically identifying impact spatters and fly spots. The objective is to assist crime scene investigators and BPA analysts to identify bloodstains at homicide scenes conveniently, rapidly and accurately, thereby furnishing scientific evidence for scene reconstruction and advancing BPA toward intelligent practices. •Objective bloodstain classification: Eliminates experience bias.•Reproducible ResNet-18 model: Adaptable, open-source.•Efficient image processing: Improves accuracy, speed.

An improved automated bloodstain pattern analysis method has been developed and validated, which utilises computer vision techniques to identify bloodstains on a plain background within a digital image. The method generates metrics relating to the individual stains as well as the overall pattern, including bloodstain pattern specific metrics such as the gamma angle, circularity, solidity, area of convergence, stain density and pattern linearity. This method provides an objective approach to the analysis of bloodstains and bloodstain patterns and can generate a wealth of quantitative data that is currently not obtainable using manual techniques or other image-based programs currently utilised in the discipline. This method will be useful to analysts and researchers investigating the application of quantitative methods to bloodstain pattern analysis. •A method that automatically analyses digital images of bloodstain spatter patterns is described.•Improved stain segmentation detects even very small stains under varied lighting conditions.•A novel approach to fitting ellipses allows for accurate BPA specific metrics to be calculated.•Quantitative data is quickly and reliably extracted from patterns increasing objectivity.