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The tufted deer (Elaphodus cephalophus), a rare ungulate species endemic to China, faces mounting conservation concerns due to habitat fragmentation, climate change, and historical overhunting. However, its current patterns of model‐predicted relative probability of occurrence and the environmental associations of its distribution remain poorly understood. In this study, we used 429 occurrence points and 28 environmental variables, refined to 11 key predictors, to predict the species' relative probability of occurrence across China using an optimized MaxEnt model. The model performed robustly, identifying six dominant environmental factors—temperature annual range, annual precipitation, mean temperature of the coldest quarter, slope, vegetation fractional cover, and human footprint index—that collectively contributed 91.6% to the model‐predicted relative probability of occurrence. Model outputs indicated that the relative probability of occurrence was associated with moderate temperature variation (25.3°C–30.4°C), bimodal precipitation patterns (725–1324 mm and 1651–1898 mm), and cooler winter temperatures (−2.0°C–9.9°C), typically found in mountainous regions. Model‐based analyses revealed that zones with moderate‐to‐high model‐predicted relative probability of occurrence are concentrated in eight provinces, with Sichuan, Guizhou, and Yunnan contributing the largest zones. Despite these extensive zones with model‐predicted relative probability of occurrence, our GAP analysis showed that 93.98% of them lie outside current protected zones overlapping with model‐predicted relative probability of occurrence areas, indicating substantial conservation gaps. Even in core provinces such as Sichuan and Guizhou, only a small fraction (≤ 10.84%) of the zones with high model‐predicted relative probability of occurrence are protected. These findings highlight the urgent need to re‐evaluate and expand protected zones networks to include zones with high model‐predicted relative probability of occurrence for tufted deer. Our study provided essential ecological insights and spatial data to guide habitat conservation, functional zoning, and long‐term management strategies for tufted deer populations in China. This study analyzed the habitat suitability of the tufted deer, a rare and endemic ungulate in China, using the MaxEnt model and found that suitable habitats are mainly concentrated in Sichuan, Guizhou, and Yunnan provinces. However, the majority of these areas are not covered by existing protected areas, highlighting the urgent need to expand conservation efforts. The findings provide crucial data and guidance for the protection and habitat management of tufted deer populations in China.

Aim Data on species occurrences are far more common than data on species abundances. However, a central goal of large‐scale ecology is to understand the spatial distribution of abundance. It has been proposed that species distribution models trained on species occurrence records may capture variation in species abundance. Here, we gauge support for relationships between species abundance and predicted climatic suitability from species distribution models, and relate the slope of this relationship to species traits, evolutionary relationships and sampling completeness. Location USA. Time period 1658–2017. Major taxa studied Mammal and tree species. Methods, Results To explore the generality of abundance–suitability relationships, we trained species distribution models on species occurrence and species abundance data for 246 mammal species and 158 tree species, and related model‐predicted occurrence probabilities to population abundance predictions. Further, we related the resulting abundance–suitability relationship coefficients to species traits, geographic range sizes, evolutionary relationships and the number of occurrence records to investigate a potential trait or sampling basis for abundance–suitability relationship detectability. We found little evidence for consistent abundance–suitability relationships in mammal (r¯ = .045) or tree (r¯ = −.005) species, finding nearly as many negative and positive relationships. These relationships had little explanatory power, and coefficients were unrelated to species traits, range size or evolutionary relationships. Main conclusions Our findings suggest that species climatic suitability based on occurrence data may not be reflected in species abundances, suggesting a need to investigate nonclimatic sources of species abundance variation.

Rainbands are essential to tropical cyclones (TCs), significantly affecting TC structure and intensity change. High‐resolution synthetic aperture radar (SAR) imagery can capture the footprints of rainbands caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we investigated the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwest Pacific than in the North Atlantic and Northeast Pacific. Plain Language Summary Rainbands are a salient feature of tropical cyclones (TCs) and are closely related to TC structure and intensity change. Synthetic aperture radar (SAR) can capture the sea surface imprint of rainbands beneath clouds caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we made 464 rainband‐annotated data. The data were mapped to grid nodes spaced at 0.027 times the radius of max winds in a coordinate system with the origin at the TC center and the y‐axis in the vertical wind shear direction. Then, the data were composited to estimate and further investigate the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwestern Pacific than in the North Atlantic and Northeast Pacific. Key Points The sea surface imprint of tropical cyclone (TC) rainbands in many synthetic aperture radar images reveals their occurrence probability The rainband‐occurrence probability is overall higher in the early morning than in the evening. The feature is more obvious in strong TCs The peak region of the probability appears farther from the TC center in the evening than in the early morning

MaxEnt is one of the most widely used tools in ecology, biogeography, and evolution for modeling and mapping species distributions using presence-only occurrence records and associated environmental covariates. Despite its popularity, the exponential model implemented by MaxEnt does not directly estimate occurrence probability, the natural quantity of interest when modeling species distributions. Instead, MaxEnt generates an index of relative habitat suitability. MaxLike, a newly introduced maximum-likelihood technique, has been shown to overcome the problem of directly estimating the probability of occurrence using presence-only data. However, the performance and relative merits of MaxEnt and MaxLike remain largely untested, especially when modeling species with relatively few occurrence data that encompass only a portion of the geographic range of the species. Using geo-referenced occurrence records for six species of ants in New England, we provide comparisons of MaxEnt and MaxLike. We show that by most quantitative metrics, the performance of MaxLike exceeds that of MaxEnt, regardless of whether MaxEnt models account for sampling bias and include greater model complexity than implemented in MaxLike. More importantly, for most species, the relative suitability index estimated by MaxEnt often was poorly correlated with the probability of occurrence estimated by MaxLike, suggesting that the two methods are estimating different quantities. For species distribution modeling, MaxLike, and similar models that are based on an explicit sampling process and that directly estimate probability of occurrence, should be considered as important alternatives to the widely-used MaxEnt framework.

In this study, we recruited 60 native Cantonese speakers to participate in a standard cross-situational word-learning task to explore the cross-situational learning effects of minimal word pairs in Cantonese Chinese. In the cross-situational word-learning task, four different types of word pairs were used: (1) a non-minimal word pair [N]; (2) a consonant minimal word pair [C]; (3) a rime minimal word pair [R]; and (4) a tone minimal word pair [T]. The results showed that the participants could learn the word-referent mapping for all word-pair types, but they performed better on the N and T types than on the other two (i.e., C and R). Together with other previous evidence, these findings suggest that Cantonese language learners can learn and encode those phonetic details while they learn the word-referent co-occurrence probabilities. The results also suggested that the tonal information seemed to be more important than the other phonological components in Cantonese Chinese word learning.

Forest fire is a primary disaster that destroys forest resources and the ecological environment, and has a serious negative impact on the safety of human life and property. Predicting the probability of forest fires and drawing forest fire risk maps can provide a reference basis for forest fire control management in Hunan Province. This study selected 19 forest fire impact factors based on satellite monitoring hotspot data, meteorological data, topographic data, vegetation data, and social and human data from 2010–2018. It used random forest, support vector machine, and gradient boosting decision tree models to predict the probability of forest fires in Hunan Province and selected the RF algorithm to create a forest fire risk map of Hunan Province to quantify the potential forest fire risk. The results show that the RF algorithm performs best compared to the SVM and GBDT algorithms with 91.68% accuracy, 91.96% precision, 92.78% recall, 92.37% F1, and 97.2% AUC. The most important drivers of forest fires in Hunan Province are meteorology and vegetation. There are obvious differences in the spatial distribution of seasonal forest fire risks in Hunan Province, and winter and spring are the seasons with high forest fire risks. The medium- and high-risk areas are mostly concentrated in the south of Hunan.

Monitoring programs increasingly are used to document the spread of invasive species in the hope of detecting and eradicating low-density infestations before they become established. However, interobserver variation in the detection and correct identification of low-density populations of invasive species remains largely unexplored. In this study, we compare the abilities of volunteer and experienced individuals to detect low-density populations of an actively spreading invasive species, and we explore how interobserver variation can bias estimates of the proportion of sites infested derived from occupancy models that allow for both false negative and false positive (misclassification) errors. We found that experienced individuals detected small infestations at sites where volunteers failed to find infestations. However, occupancy models erroneously suggested that experienced observers had a higher probability of falsely detecting the species as present than did volunteers. This unexpected finding is an artifact of the modeling framework and results from a failure of volunteers to detect low-density infestations rather than from false positive errors by experienced observers. Our findings reveal a potential issue with site occupancy models that can arise when volunteer and experienced observers are used together in surveys.

Site occupancy models have been developed that allow for imperfect species detection or \"false negative\" observations. Such models have become widely adopted in surveys of many taxa. The most fundamental assumption underlying these models is that \"false positive\" errors are not possible. That is, one cannot detect a species where it does not occur. However, such errors are possible in many sampling situations for a number of reasons, and even low false positive error rates can induce extreme bias in estimates of site occupancy when they are not accounted for. In this paper, we develop a model for site occupancy that allows for both false negative and false positive error rates. This model can be represented as a two-component finite mixture model and can be easily fitted using freely available software. We provide an analysis of avian survey data using the proposed model and present results of a brief simulation study evaluating the performance of the maximum-likelihood estimator and the naive estimator in the presence of false positive errors.

Heating, ventilation, and air-conditioning (HVAC) systems are large-scale distributed systems that can be subject to multiple faults affecting the electronics, sensors, and actuators, potentially causing high energy consumption, occupant discomfort, degraded indoor air quality and risk to critical infrastructure. Fault injection (FI) is an effective experimental method for the validation and dependability evaluation of such HVAC systems. Today’s FI frameworks for HVAC systems are still based on a single fault hypothesis and do not provide insights into dependability in the case of multiple faults. Therefore, this paper presents modeling patterns of numerous faults in HVAC systems based on data from field failure rates and maintenance records. The extended FI framework supports the injection of multiple faults with exact control of the timing, locality, and values in fault-injection vectors. A multi-dimensional fault model is defined, including the probability of the occurrence of different sensor and actuator faults. Comprehensive experimental results provide insights into the system’s behavior for concrete example scenarios using patterns of multiple faults. The experimental results serve as a quantitative evaluation of key performance indicators (KPI) such as energy efficiency, air quality, and thermal comfort. For example, combining a CO2 sensor fault with a heater actuator fault increased energy consumption by more than 70%.

Questions Small islands are outstanding model systems to study community assembly. Due to harsher environmental conditions on smaller islands compared to larger ones, environmental filtering may preclude some species, potentially resulting in island size‐dependent species pools. We tested whether the species pool size follows a similar species–area relationship as the observed richness. This can provide new insight into community assembly processes and the elusive small‐island effect (SIE), which states that species richness on smaller islands is less dependent on area than on larger islands. Location Raja Ampat Archipelago, Indonesia. Methods We studied the woody vegetation on sixty small islands ranging from 3 m2 to 11,806 m2. For each recorded species, we estimated its area requirements and compared them against random colonization models. We developed a novel method to calculate probabilistic species pools for each island. We compared different species–area models for observed species richness and our index of species pool size to test whether the SIE results from differences in species pool size. Results We found that most species were restricted to islands significantly larger than expected from random colonization. The occurrence probability of all species increased with island size, indicating a lack of species that are specialized to the conditions on small islands. We found a SIE in observed species richness, but not in species pool size. Conclusion Woody plants in the studied island system have specific requirements that are linked to island area and determine island‐specific species pools. Lower community completeness on smaller islands compared to larger ones indicated that the SIE is shaped by local limiting processes that have no impact on the species pool, but control how much of it is realized on an island. Together, these results clearly indicate non‐random plant community assembly on small islands. We estimated species‐area requirements and developed a new method to calculate probabilistic species pools for plants on small islands. We found evidence that local limiting processes on small islands restrict most species to larger ones, thereby causing island‐specific species pools. Knowledge about species‐area requirements and species pools are useful to investigate community assembly processes on islands.