Xianxialing Nature Reserve extends between 28°15’47″N and 28°27’40″N latitude and 118°20’14″E and 118°40’50″E longitude. The area has an average temperature of 15.0°C, annual precipitation of 1650–2200 mm and an average altitude of 1,000 m. Its complex topography contributes to four distinct seasons and pronounced local variation in microclimatic conditions.

For the observational study, researchers deployed 97 infrared-triggered cameras across the reserve between January 2021 and November 2023. Camera stations were preferentially positioned in open understorey near water sources and at locations selected using visible signs of animals, including trails, faeces, nests, foraging marks, scratches and feathers. The cameras were accompanied by metadata recording coordinates, station identification, timestamps, elevation and habitat type.

The survey constituted the reserve’s first systematic camera-trap assessment of threatened mammal activity rhythms since its establishment, according to the study. The results recorded 12 mammal species, including the nationally protected Tibetan Macaque, Rhesus Macaque, Black Muntjac and Chinese Serow. A rarefaction curve indicated sufficient sampling effort, while Reeves’ Muntjac dominated the detected small- to medium-sized ground-dwelling mammals, a pattern consistent with findings from other subtropical reserves in China.

Activity rhythms varied among species and seasons. Chinese Ferret-badger, Chinese Hare and Masked Palm Civet showed predominantly nocturnal activity, consistent with the nocturnal foraging niches described for those species. Reeves’ Muntjac and Black Muntjac displayed bimodal, crepuscular patterns, with activity concentrated around dawn and dusk. These patterns were interpreted as possible forms of temporal niche optimisation that allow animals to use favourable light and temperature conditions while reducing exposure to potential daytime risks.

Hog Badger showed a pronounced midday activity peak during winter. The authors associated this pattern with an adaptive response to limited winter food availability, suggesting that increased daytime activity may help meet energy demands. This interpretation remains a proposed explanation rather than a result directly tested by the camera-trap observations, and the supplied account does not report numerical estimates of activity intensity or statistical uncertainty.

Seasonal variation in elevation was also observed for Masked Palm Civet, which preferred higher-altitude habitats in spring and summer and shifted toward lower areas in autumn and winter. The authors described this “upward in summer, downward in winter” pattern as potentially associated with seasonal temperature variation. By contrast, Hog Badger, Black Muntjac, Chinese Hare and Chinese Serow were reported at high elevations throughout the year, indicating differing altitudinal strategies among species sharing the landscape.

These divergent strategies may contribute to resource partitioning across altitudinal zones and thereby reduce interspecific competition, although the camera-trap study did not establish that mechanism causally. Its findings are observational and are based on detections from 97 camera stations rather than a census of every mammal in the reserve. The available account also does not provide species-level detection totals, Relative Abundance Index values or the complete statistical results, so the dataset is best interpreted as a baseline characterization rather than evidence of population trends.

Overall, the monitoring identified substantial mammalian diversity in Xianxialing Nature Reserve and showed clear interspecific and seasonal differences in daily activity, together with contrasting elevational patterns. The authors describe the dataset as a baseline for ecological research, monitoring and conservation management. They also call for later studies to integrate high-resolution vegetation maps, quantified anthropogenic-disturbance data and climate data to examine the ecological mechanisms behind the observed patterns.

Subject of Research: Camera-trap monitoring of mammalian diversity, daily activity rhythms and seasonal elevational patterns in Xianxialing Nature Reserve.

Article Title: Long-term camera trap monitoring of mammalian diversity and activity rhythms of threatened species in Xianxialing Nature Reserve, Zhejiang Province, China

Article References: Yu, Z.-C., Wang, X.-Y., Gao, C., Wang, K., Wang, M.-F., Gao, B.-W., Zhao, W., Wu, S.-H., Xu, Z.-P., Zhou, X., Xu, L.-L., Yu, J., Nneji, L., Chen, J.-M., Jin, W., & Lee, P. S. (2026). Long-term camera trap monitoring of mammalian diversity and activity rhythms of threatened species in Xianxialing Nature Reserve, Zhejiang Province, China. Biodiversity Data Journal, 14, Article e208779. https://doi.org/10.3897/bdj.14.e208779

Image Credits: AI Generated

DOI: 10.3897/BDJ.14.e208779

Keywords: camera trapping, mammal diversity, threatened species, activity rhythms, Xianxialing Nature Reserve, Zhejiang Province, wildlife monitoring, altitudinal migration, seasonal behaviour, Long-term, camera, trap

The headline finding is sobering for hospital administrators. When the researchers modeled the joint information from the three dimensions of the Nurse Innovative Behavior Scale, the largest group of nurses, 61.85 percent of the sample, or 261 individuals, landed in a profile the authors labeled a relative low-innovation level. A second group, 31.99 percent or 135 nurses, showed a relative moderate level, while only 6.16 percent, just 26 nurses, fell into the relative high-innovation profile. The authors are careful to stress that these labels are sample-relative descriptors rather than validated clinical or managerial cutoffs, meaning the categories describe where nurses stood within this particular dataset, not universal thresholds of innovation.

Latent profile analysis is a person-centered statistical method that differs from the more common practice of simply averaging questionnaire scores. Instead of treating every nurse as a point on a single continuous scale, LPA looks for subgroups of individuals who answer the scale’s dimensions in similar patterns. In this study, the observed indicators were the mean scores of the three dimensions of innovative behavior, and the researchers compared competing models using fit statistics such as the Bayesian information criterion. That technical detail matters, because the final interpretation of the study hinges on it: the data did not cleanly demand a three-type solution.

Indeed, the authors report that a continuous one-factor model actually achieved a lower BIC than the three-profile model, while a four-profile solution showed better information-criterion fit than the three-profile one. In plain terms, the statistical evidence suggests that innovative behavior among these nurses varies along a continuum, and the three-level stratification is best understood as a descriptive convenience rather than proof that nurses come in three discrete types. The team explicitly cautions that the three-profile solution should be interpreted as empirical stratification along a continuous dimension, not as a mutually exclusive latent structure. This kind of methodological transparency is relatively rare in person-centered nursing research, where profile solutions are often presented as if they revealed natural categories.

What did the profiles differ on? The three groups showed statistically significant differences in both total and dimensional scores of organizational justice and job embeddedness, with all comparisons reaching P values below 0.001. Organizational justice refers to employees’ perceptions of fairness in how decisions are made, how procedures are applied, and how people are treated, while job embeddedness captures the web of links, fit, and sacrifice that keeps people anchored in their jobs and communities. Both constructs have long been theorized as resources that could fuel discretionary effort, including the risky, unrewarded work of championing a new clinical practice.

To move beyond simple group comparisons, the researchers ran an exploratory multinomial logistic regression examining which factors predicted membership in the higher-innovation profiles relative to the low-innovation reference group. Organizational justice emerged as the most consistent and robust correlate. For each one-point increase in organizational justice scores, the odds of belonging to the moderate-innovation profile rather than the low-innovation profile rose by a factor of 1.081, with a 95 percent confidence interval of 1.044 to 1.120 and a P value below 0.001. The association was even stronger for the top profile: each one-point increase in perceived justice multiplied the odds of high-innovation membership by 1.285, with a confidence interval of 1.162 to 1.422.

Administrative position also mattered. Nurses who held no formal management role had roughly half the odds of belonging to the moderate-innovation profile compared with nurses at head nurse level or above, with an odds ratio of 0.497, a 95 percent confidence interval of 0.259 to 0.952, and a P value of 0.035. This finding hints at a familiar dynamic in hospital hierarchies: nurses with leadership titles may have more access to resources, project approval channels, and professional networks that make turning an idea into a implemented change feasible. Bedside nurses, however motivated, may simply lack the organizational leverage to push innovations through.

Perhaps the most striking negative results concern the factors that did not survive statistical adjustment. Job embeddedness, participation in research training, and monthly income were not significantly associated with profile membership once other variables were controlled. That job embeddedness dropped out is notable, because embeddedness is often assumed to be a driver of discretionary contributions like innovation. The result suggests that simply keeping nurses attached to their jobs is not the same as enabling them to change how care is delivered. Similarly, the null finding for research training challenges the assumption that formal research exposure automatically translates into bedside innovation, and the income result suggests that financial incentives alone may not unlock creative behavior in this population.

The authors are appropriately restrained about what these associations can and cannot mean. Because the study is cross-sectional, the between-profile comparisons are descriptive and cannot establish temporal or causal relationships. It is entirely possible that nurses who innovate come to perceive their organizations as fairer, rather than fairness breeding innovation, or that some unmeasured factor influences both. The regression estimates were also based on hard class assignment, meaning each nurse was treated as a definite member of one profile even though latent profile analysis inherently involves classification uncertainty. The high-innovation profile contained only 26 nurses, which limits the precision of estimates involving that group. Whether organizational justice can serve as a modifiable intervention target, the authors conclude, requires prospective or intervention-based evaluation rather than inference from a single snapshot.

Even with those caveats, the study carries a clear message for nursing management. In this sample, innovative behavior was predominantly at a relatively low level, and the factor most consistently linked to higher levels was something managers can plausibly influence: the perceived fairness of the organizational environment. The researchers suggest that a fair and supportive workplace may warrant evaluation as a potential management priority, and that profile stratification, while not a validated diagnostic tool, may prove useful for descriptive management and hypothesis generation. If subsequent intervention studies confirm that strengthening procedural and distributive justice raises the odds of nurses developing and implementing new ideas, the payoff could extend well beyond innovation metrics, since the quality and safety of patient care in hospitals ultimately depends on the people closest to the bedside being able to improve what they see every day.

Subject of Research: Latent profile analysis of innovative behavior and its associated factors among clinical nurses in China

Article Title: Innovative behavior among clinical nurses: empirical level stratification and associated factors in a cross-sectional study in Zhejiang Province, China

Article References: Cao, K., Lin, L., Yu, R., Zheng, H., & Chen, F. (2026). Innovative behavior among clinical nurses: empirical level stratification and associated factors in a cross-sectional study in Zhejiang Province, China. BMC Nursing. https://doi.org/10.1186/s12912-026-05474-2

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05474-2

Keywords: clinical nurses, innovative behavior, latent profile analysis, organizational justice, job embeddedness, nursing management, cross-sectional study, Zhejiang Province, BMC Nursing, tertiary hospitals, nursing research, workplace fairness

Zhejiang presents an ideal and demanding test case. This coastal province in southeastern China is home to roughly 66.7 million people and an economy exceeding 9 trillion CNY in 2024, yet nearly 75 percent of its land is covered by hills and mountains, with only about 20 percent plains and a sliver of rivers and lakes. Rapid urbanization compounds the challenge, since weather stations are sparse, unevenly distributed, and subject to relocations, instrumentation changes, and gaps in the historical record. Ground observations offer accuracy but limited spatial coverage; satellite land surface temperature products offer detail but generally begin only in the early 2000s and are vulnerable to cloud cover, terrain shading, and atmospheric interference; reanalysis products such as ERA5 provide continuity but at coarse spatial resolutions, typically around 0.25 degrees or coarser, far too blunt to resolve the fine thermal texture of mountainous landscapes.

The team’s solution is an elegant two-part construction they call a spatial-background residual framework. First, they built a high-resolution climatological baseline from an existing hourly, 1-kilometer gridded temperature dataset covering 2008 to 2018, which had itself been developed using the INCA data-fusion framework with reanalysis fields and dense automatic weather station observations. This baseline serves purely as a spatial background, encoding how temperature varies across the terrain on each calendar day of the year. Second, daily temperature residuals—the departures of each station observation from that climatological expectation—were calculated for every meteorological station across the full 1961 to 2020 period. Because these residuals are computed directly from observed temperatures, they inherently preserve the long-term warming trend and interannual variability, while the baseline contributes the terrain-driven spatial detail. Summing the interpolated residual field with the baseline yields the finished reconstruction.

A critical methodological question was how best to interpolate those daily residuals across space. The researchers evaluated three schemes representing different levels of topographic correction and complexity: plain inverse distance weighting, or IDW, which relies only on spatial proximity; a lapse-rate-adjusted version of IDW, in which station temperatures are first corrected to grid-cell elevation using a fixed adiabatic lapse rate of 6.0 degrees Celsius per kilometer; and a multiple linear regression incorporating longitude, latitude, and elevation as predictors. Using leave-one-out cross-validation, in which each station is successively withheld and predicted from the others, the team assessed performance with mean absolute error, root-mean-square error, and the coefficient of determination across decades, seasons, and elevation zones.

The verdict was striking: the simplest method won. IDW without any lapse-rate correction consistently delivered the lowest errors and highest skill, achieving the best performance at roughly 68 percent of stations for daily minimum temperature, 70.7 percent for mean temperature, and 74.7 percent for maximum temperature. In a representative example from 1971 to 1980, IDW reconstructed minimum temperatures with a mean absolute error of just 0.73 degrees Celsius and an R-squared of 0.987, comfortably beating both rivals. The reason lies in the behavior of the lapse rate itself. Analysis of 60 years of observations revealed that near-surface temperature lapse rates in Zhejiang are strongly non-stationary: they peak in summer, with minimum-temperature lapse rates exceeding 7.0 degrees Celsius per kilometer in mountainous areas during July and August, yet collapse toward zero or even turn negative in winter lowlands, where temperature inversions prevail. Applying a fixed correction therefore risks systematic, elevation-related biases—a caution with implications well beyond Zhejiang.

The errors that do remain follow clear and intelligible patterns. Reconstruction accuracy improved steadily from the 1960s onward as station density grew, and summer months outperformed winter months because spatial temperature gradients are weaker in warm weather. Low-elevation areas below 400 meters consistently yielded smaller errors than high-elevation zones, where complex terrain and sparse instrumentation conspire against interpolation. Spatially, larger uncertainties cluster in the mountainous southwest, including parts of Lishui and western Wenzhou, while the plains around Hangzhou, Shaoxing, and Jinhua show excellent agreement, with most stations achieving R-squared values above 0.90 and many above 0.95. Across the entire 60-year span, the annual mean error for all three temperature variables stayed within plus or minus 0.1 degrees Celsius, with no systematic drift across decades—a testament to the temporal stability of the method.

Perhaps the most compelling validation came from real disasters. The team tested ZJ-DAT against two extreme events from 2007, using more than a thousand automatic weather stations as independent ground truth while deliberately excluding any stations that had contributed to the reconstruction. During the cold wave of 4 to 9 March 2007, ZJ-DAT tracked the south-to-north advance of the cold air, accurately reproducing the observed cold centers around Lishui, with R-squared values of 0.65 to 0.79 and root-mean-square errors of 1.01 to 1.67 degrees Celsius. By comparison, the CDAT national dataset managed only moderate agreement, while ERA5 performed poorly, with near-zero or negative correlations and errors approaching 3 degrees Celsius. The heat wave of 30 June to 10 July 2007 told the same story: ZJ-DAT best resolved the core hot zones above 37 degrees Celsius over Jinhua, Shaoxing, and Ningbo and the inland-coastal thermal contrast, while CDAT smoothed away local extremes and ERA5 drifted with warm biases and excessive homogenization. Case studies of cold and heat events in January and July 2020 at four environmentally distinct stations—an island, a mountain site, and two plain stations—further confirmed the reconstruction’s fidelity, with discrepancies generally under 2 degrees Celsius.

Beyond validation, the dataset enabled a first-of-its-kind hazard assessment for the province. Using Gumbel distribution analysis of return periods, the researchers mapped the intensity of extreme cold and heat expected at 5-, 20-, and 50-year recurrence intervals. The results expose stark geographic contrasts in climate risk. Extreme low-temperature hazards concentrate in the northwestern inland regions, where 50-year minimum temperatures plunge below minus 15 degrees Celsius, while the southeastern coast stays comparatively mild. Extreme heat hazards show the opposite pattern, dominated by low-altitude basins in central and northern Zhejiang, where 50-year maximum temperatures climb above 43 to 44 degrees Celsius—figures that carry sobering weight given projections of accelerating heatwave duration under global warming. These maps, grounded in kilometer-scale temperature data rather than coarse reanalysis, offer planners a far sharper picture of where adaptation investments are most needed.

The authors are candid about limitations. Anchoring the reconstruction to a climatology drawn from 2008 to 2018 means the reference field does not reflect the climate state of earlier decades, though because it functions only as a spatial scaffold while temporal signals come from station residuals, warming trends and variability remain intact. The team suggests that future refinements could employ temporally adaptive reference fields. The broader significance, however, is clear: ZJ-DAT demonstrates that a simple, computationally efficient interpolation of station anomalies, layered onto a modern high-resolution climatology, can outperform more elaborate schemes in complex terrain—provided the scheme is chosen with local lapse-rate physics in mind. The framework, and the publicly available dataset released through Zenodo, is designed to be transferable to other topographically complex, observation-limited regions, offering a practical foundation for climate monitoring, extreme-event risk assessment, and adaptation planning as the planet continues to warm.

Subject of Research: High-resolution daily near-surface air temperature reconstruction for Zhejiang Province, China, from 1961 to 2020 using statistical residual interpolation

Article Title: A high-resolution daily temperature reconstruction for Zhejiang Province during 1961–2020 using statistical residual interpolation

Article References: Li, Y., Guo, H., Dong, M., Wu, J., Deng, F., Chen, Y., & Chen, F. (2026). A high-resolution daily temperature reconstruction for Zhejiang Province during 1961–2020 using statistical residual interpolation. Theoretical and Applied Climatology, 157(10), Article 632. https://doi.org/10.1007/s00704-026-06521-3

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06521-3

Keywords: Zhejiang Province, air temperature reconstruction, inverse distance weighting, spatial interpolation, temperature lapse rate, extreme temperature events, heat wave, cold wave, climate monitoring, hazard assessment, ERA5, high-resolution gridded dataset