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Implementation of wildfire- and climate-adaptation strategies in seasonally dry forests of western North America is impeded by numerous constraints and uncertainties. After more than a century of resource and land use change, some question the need for proactive management, particularly given novel social, ecological, and climatic conditions. To address this question, we first provide a framework for assessing changes in landscape conditions and fire regimes. Using this framework, we then evaluate evidence of change in contemporary conditions relative to those maintained by active fire regimes, i.e., those uninterrupted by a century or more of human-induced fire exclusion. The cumulative results of more than a century of research document a persistent and substantial fire deficit and widespread alterations to ecological structures and functions. These changes are not necessarily apparent at all spatial scales or in all dimensions of fire regimes and forest and nonforest conditions. Nonetheless, loss of the once abundant influence of low- and moderate-severity fires suggests that even the least fire-prone ecosystems may be affected by alteration of the surrounding landscape and, consequently, ecosystem functions. Vegetation spatial patterns in fire-excluded forested landscapes no longer reflect the heterogeneity maintained by interacting fires of active fire regimes. Live and dead vegetation (surface and canopy fuels) is generally more abundant and continuous than before European colonization. As a result, current conditions are more vulnerable to the direct and indirect effects of seasonal and episodic increases in drought and fire, especially under a rapidly warming climate. Long-term fire exclusion and contemporaneous social-ecological influences continue to extensively modify seasonally dry forested landscapes. Management that realigns or adapts fire-excluded conditions to seasonal and episodic increases in drought and fire can moderate ecosystem transitions as forests and human communities adapt to changing climatic and disturbance regimes. As adaptation strategies are developed, evaluated, and implemented, objective scientific evaluation of ongoing research and monitoring can aid differentiation of warranted and unwarranted uncertainties.

Eigenvector-mapping methods such as Moran’s eigenvector maps (MEM) are derived from a spatial weighting matrix (SWM) that describes the relations among a set of sampled sites. The specification of the SWM is a crucial step, but the SWM is generally chosen arbitrarily, regardless of the sampling design characteristics. Here, we compare the statistical performances of different types of SWMs (distance-based or graph-based) in contrasted realistic simulation scenarios. Then, we present an optimization method and evaluate its performances compared to the arbitrary choice of the most-widely used distance-based SWM. Results showed that the distance-based SWMs generally had lower power and accuracy than other specifications, and strongly underestimated spatial signals. The optimization method, using a correction procedure for multiple tests, had a correct type I error rate, and had higher power and accuracy than an arbitrary choice of the SWM. Nevertheless, the power decreased when too many SWMs were compared, resulting in a trade-off between the gain of accuracy and the loss of power. We advocate that future studies should optimize the choice of the SWM using a small set of appropriate candidates. R functions to implement the optimization are available in the adespatial package and are detailed in a tutorial.

The motor imagery (MI)-based brain-computer interface (BCI) is one of the most popular BCI paradigms. Common spatial pattern (CSP) is an effective algorithm for decoding MI-related electroencephalogram (EEG) patterns. However, it highly depends on the selection of EEG frequency bands. To address this problem, previous researchers often used a filter bank to decompose EEG signals into multiple frequency bands before applying the traditional CSP. This study proposed a novel method, i.e., transformed common spatial pattern (tCSP), to extract the discriminant EEG features from multiple frequency bands after but not before CSP. To verify its effectiveness, we tested tCSP on a dataset collected by our team and a public dataset from BCI competition III. We also performed an online evaluation of the proposed method. As a result, for the dataset collected by our team, the classification accuracy of tCSP was significantly higher than CSP by about 8% and filter bank CSP (FBCSP) by about 4.5%. The combination of tCSP and CSP further improved the system performance with an average accuracy of 84.77% and a peak accuracy of 100%. For dataset IVa in BCI competition III, the combination method got an average accuracy of 94.55%, which performed best among all the presented CSP-based methods. In the online evaluation, tCSP and the combination method achieved an average accuracy of 80.00 and 84.00%, respectively. The results demonstrate that the frequency band selection after CSP is better than before for MI-based BCIs. This study provides a promising approach for decoding MI EEG patterns, which is significant for the development of BCIs.

Knowledge about species abundance across broad spatial areas is crucial for unraveling ecological processes. Yet, abundance estimation often demands extensive sampling effort associated with logistical challenges. Using suitability values obtained from species distribution models (based on species' presence data) as a proxy for abundance has garnered interest during the last decades. Previous studies suggest a triangular relationship between species abundance and suitability. Specifically, higher suitability can display both low and high abundances, while low suitability only low abundances. This triangular pattern arises because suitability models often fail to consider limiting factors that drive abundance. In this study, we investigate the effect of spatial scale and pattern shaping this relationship. We use a simulation study and a case study to explore how these factors affect the abundance–suitability relationship. The effects of spatial scale are represented by three model levels: 1) only broad‐scale covariates, 2) broad and intermediate covariates, and 3) broad, intermediate and local covariates. The effects of spatial patterns are characterized by two different species distribution shapes: aggregated and uniform. Our findings reveal that models integrating local‐scale covariates and species exhibiting more aggregated spatial patterns show a stronger relationship. Additionally, we observe an interaction between a species' spatial pattern and model scale. For aggregated species, the abundance–suitability relationship benefits most notably from the addition of intermediate‐scale covariates. In contrast, for uniform species, the benefit remains consistent regardless of whether intermediate‐ or local‐scale covariates are added. Our results underscore the importance of considering both methodological and ecological factors to improve proxies for abundance derived from suitability models. We highlight the need for considering information operating at a local scale to make reliable inferences about species abundance from suitability models and suggest potential strategies for doing it.

The ecological processes that create spatial patterns have been examined by direct measurement and through measurement of patterns resulting from experimental manipulations. But in many situations, creating experiments and direct measurement of spatial processes can be difficult or impossible. Here, we identify and define a rapidly emerging alternative approach, which we formalize as \"space as a surrogate\" for unmeasured processes, that is used to maximize inference about ecological processes through the analysis of spatial patterns or spatial residuals alone. This approach requires three elements to be successful: a priori hypotheses, ecological theory and/or knowledge, and precise spatial analysis. We offer new insights into a long-standing debate about process—pattern links in ecology and highlight six recent studies that have successfully examined spatial patterns to understand a diverse array of processes: competition in forest-stand dynamics, dispersal of freshwater fish, movement of American marten, invasion mechanisms of exotic trees, dynamics of natural disturbances, and tropical-plant diversity. Key benefits of using space as a surrogate can be found where experimental manipulation or direct measurements are difficult or expensive to obtain or not possible. We note that, even where experiments can be performed, this procedure may aid in measuring the in situ importance of the processes uncovered through experiments.

Facilitation is a major force shaping the structure and diversity of plant communities in terrestrial ecosystems. Detecting positive plant-plant interactions relies on the combination of field experimentation and the demonstration of spatial association between neighboring plants. This has often restricted the study of facilitation to particular sites, limiting the development of systematic assessments of facilitation over regional and global scales. Here we explore whether the frequency of plant spatial associations detected from high-resolution remotely sensed images can be used to infer plant facilitation at the community level in drylands around the globe. We correlated the information from remotely sensed images freely available through Google Earth with detailed field assessments, and used a simple individual-based model to generate patch-size distributions using different assumptions about the type and strength of plant-plant interactions. Most of the patterns found from the remotely sensed images were more right skewed than the patterns from the null model simulating a random distribution. This suggests that the plants in the studied drylands show stronger spatial clustering than expected by chance. We found that positive plant co-occurrence, as measured in the field, was significantly related to the skewness of vegetation patch-size distribution measured using Google Earth images. Our findings suggest that the relative frequency of facilitation may be inferred from spatial pattern signals measured from remotely sensed images, since facilitation often determines positive co-occurrence among neighboring plants. They pave the road for a systematic global assessment of the role of facilitation in terrestrial ecosystems.

Urban green spaces (UGSs) play a crucial role in supporting urban ecological systems and improving human well-being in cities. The spatial patterns of UGS are vital bases for analyzing various ecological processes. However, few studies have investigated morphological UGS patterns, especially in high-density cities. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) in China is one of the four major bay areas in the world. The aim of this study was to investigate the patterns and distributions of UGS in the core GBA cities (Guangzhou, Shenzhen, Zhuhai, Hong Kong, and Macao), and discuss the shortcomings and potential environmental impacts of the contemporary patterns of UGS. Morphological spatial pattern analysis (MSPA) was used to analyze the spatial UGS pattern. Seven MSPA metrics (core, islet, perforation, edge, loop, bridge, and branch) were assessed to measure morphological UGS patterns. The results showed that: (1) Hong Kong has the highest quality habitat, with a large and continuous distribution of UGSs, and a few smaller green spaces scattered in built-up areas; (2) Guangzhou’s UGSs are unevenly distributed, with large green spaces concentrated in the northern part of the city and many small, scattered green spaces distributed in built-up areas, demonstrating the most prominent pattern of green space fragmentation; (3) green space patches in the Shenzhen–Hong Kong region exhibit a relatively complex form; and (4) the UGS in Zhuhai–Macao is relatively discrete, and its connectivity is relatively low. These findings not only improve the depth of understanding of the spatial pattern of UGS in the GBA, but also confirm the applicability of MSPA in the analysis of spatial patterns of UGS.

The number of species shared by two or more sites is a fundamental measure of spatial variation in species composition. As more sites are included in the comparison of species composition, the average number of species shared across them declines, with a rate increasingly dependent on only the most widespread species. In over 80% of empirical communities, models of decline in shared species across multiple sites (multisite similarity decline) follow one of two distinct forms. An exponential form is assumed to reflect stochastic assembly and a power law form niche‐based sorting, yet these explanations are largely untested, and little is known of how the two forms arise in nature. Using simulations, we first show that the distribution of the most widespread species largely differentiates the two forms, with the power law increasingly favored where such species occupy more than ~75% of sites. We reasoned the less cosmopolitan distribution of widespread species within exponential communities would manifest as differences in community biodiversity properties, specifically more aggregated within‐species distributions, less even relative abundance distributions, and weaker between‐species spatial associations. We tested and largely confirmed these relationships using 80 empirical datasets, suggesting that the form of multisite similarity decline offers a basis to predict how landscape‐scale loss or gain of widespread species is reflected in different local‐scale community structures. Such understanding could, for example, be used to predict changes in local‐scale competitive interactions following shifts in widespread species' distributions. We propose multiple explanations for the origin of exponential decline, including high among‐site abiotic variation, sampling of highly specialized (narrow niche width) taxa, and strong dispersal limitation. We recommend these are evaluated as alternative hypotheses to stochastic assembly. Comparison of diversity patterns for the two dominant forms of multisite similarity decline (a) decline in similarity (b) species accumulation.

We aimed to compare the properties of seed banks in different types of Robinia pseudoacacia stands and different substratum layers. We established four Black locust plots (each 50 × 50 m) that included two second-generation stands and two third-generation stands. Spatial coordinates, diameter at breast height, and the heights of all trees were measured in the four plots. In each plot, we set a total of 259 points using the regular grid design method. At these points, we sampled the seed banks in the litter and soil (0-5 cm) layers. The coordinates of the 259 points were recorded. After the samples had been collected and screened, a germination trial was performed using the collected seeds from the different layers and stands. We used variogram and kriging interpolation geostatistical methods to analyze the distribution of the seed banks. A kernel density estimation map was generated to examine the relationship between the seed bank and trees in each stand. The results showed that seed bank density was high in the four stands (4005-7325 seeds.m-2), and was higher in the third-generation stands (6085 and 7325 seeds.m-2) than in the second-generation stands (4005 and 5659 seeds.m-2). The seed bank density in the litter layer (3225 seeds.m-2) exceeded that in the soil layer (2164 seeds.m-2). The spatial pattern of the seed banks varied among different stands and was positively correlated with the distribution of trees in each stand. Furthermore, we found that spatial autocorrelation in the seed banks occurred at a variety of scales. Seeds in the litter layer were significantly more active than those in the soil layer; the germination rate varied from 6.67% to 28.89%. The findings of this study suggest that the Robinia pseudoacacia plantation in the Luoning area may exhibit potential for regeneration from seeds, and this will be the focus of our future studies.

This study presents an innovative approach to mitigating the urban heat island (UHI) effect by constructing a cold island spatial pattern (CSP) from the perspective of landscape connectivity, integrating three-dimensional (3D) urban morphology and meteorological factors for the first time. Unlike traditional studies that focus on isolated patches or single-city scales, we propose a hierarchical framework for urban agglomerations, combining morphological spatial pattern analysis (MSPA), landscape connectivity assessment, and circuit theory to a construct CSP at the scale of urban agglomeration. By incorporating wind environment data and 3D building features (e.g., height, density) into the resistance surface, we enhance the accuracy of cooling network identification, revealing 39 cold island sources, 89 cooling corridors, and optimal corridor widths (600 m) in the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXUA). Ultimately, a three-tiered heat island mitigation framework for urban agglomerations was established based on the CSP. This study offers an innovative perspective on urban climate adaptability planning within the context of contemporary urbanization. Our methodology and findings provide critical insights for future studies to integrate multiscale, multidimensional, and climate-adaptive approaches in urban thermal environment governance, fostering sustainable urbanization under escalating climate challenges.