Integrating citizen‐science and planned‐survey data improves species distribution estimates

Cancer survival classification using integrated data sets and intermediate information.

BackgroundAs a common infectious disease, hand, foot and mouth disease (HFMD) is affected by multiple environmental and socioeconomic factors, and its pathogenesis is complex. Furthermore, the transmission of HFMD is characterized by strong spatial clustering and autocorrelation, and the classical statistical approach may be biased without consideration of spatial autocorrelation. In this paper, we propose to embed spatial characteristics into a spatiotemporal additive model to improve HFMD incidence assessment.MethodsUsing incidence data (6439 samples from 137 monitoring district) for Shandong Province, China, along with meteorological, environmental and socioeconomic spatial and spatiotemporal covariate data, we proposed a spatiotemporal mixed model to estimate HFMD incidence. Geo-additive regression was used to model the non-linear effects of the covariates on the incidence risk of HFMD in univariate and multivariate models. Furthermore, the spatial effect was constructed to capture spatial autocorrelation at the sub-regional scale, and clusters (hotspots of high risk) were generated using spatiotemporal scanning statistics as a predictor. Linear and non-linear effects were compared to illustrate the usefulness of non-linear associations. Patterns of spatial effects and clusters were explored to illustrate the variation of the HFMD incidence across geographical sub-regions. To validate our approach, 10-fold cross-validation was conducted.ResultsThe results showed that there were significant non-linear associations of the temporal index, spatiotemporal meteorological factors and spatial environmental and socioeconomic factors with HFMD incidence. Furthermore, there were strong spatial autocorrelation and clusters for the HFMD incidence. Spatiotemporal meteorological parameters, the normalized difference vegetation index (NDVI), the temporal index, spatiotemporal clustering and spatial effects played important roles as predictors in the multivariate models. Efron’s cross-validation R2 of 0.83 was acquired using our approach. The spatial effect accounted for 23% of the R2, and notable patterns of the posterior spatial effect were captured.ConclusionsWe developed a geo-additive mixed spatiotemporal model to assess the influence of meteorological, environmental and socioeconomic factors on HFMD incidence and explored spatiotemporal patterns of such incidence. Our approach achieved a competitive performance in cross-validation and revealed strong spatial patterns for the HFMD incidence rate, illustrating important implications for the epidemiology of HFMD.

Few studies link habitat to grizzly bear Ursus arctos abundance and these have not accounted for the variation in detection or spatial autocorrelation. We collected and genotyped bear hair in and around Glacier National Park in northwestern Montana during the summer of 2000. We developed a hierarchical Markov chain Monte Carlo model that extends the existing occupancy and count models by accounting for (1) spatially explicit variables that we hypothesized might influence abundance; (2) separate sub-models of detection probability for two distinct sampling methods (hair traps and rub trees) targeting different segments of the population; (3) covariates to explain variation in each sub-model of detection; (4) a conditional autoregressive term to account for spatial autocorrelation; (5) weights to identify most important variables. Road density and per cent mesic habitat best explained variation in female grizzly bear abundance; spatial autocorrelation was not supported. More female bears were predicted in places with lower road density and with more mesic habitat. Detection rates of females increased with rub tree sampling effort. Road density best explained variation in male grizzly bear abundance and spatial autocorrelation was supported. More male bears were predicted in areas of low road density. Detection rates of males increased with rub tree and hair trap sampling effort and decreased over the sampling period. We provide a new method to (1) incorporate multiple detection methods into hierarchical models of abundance; (2) determine whether spatial autocorrelation should be included in final models. Our results suggest that the influence of landscape variables is consistent between habitat selection and abundance in this system.

With the advance of methods for estimating species distribution models has come an interest in how to best combine datasets to improve estimates of species distributions. This has spurred the development of data integration methods that simultaneously harness information from multiple datasets while dealing with the specific strengths and weaknesses of each dataset.We outline the general principles that have guided data integration methods and review recent developments in the field. We then outline key areas that allow for a more general framework for integrating data and provide suggestions for improving sampling design and validation for integrated models.Key to recent advances has been using point‐process thinking to combine estimators developed for different data types. Extending this framework to new data types will further improve our inferences, as well as relaxing assumptions about how parameters are jointly estimated. These along with the better use of information regarding sampling effort and spatial autocorrelation will further improve our inferences.Recent developments form a strong foundation for implementation of data integration models. Wider adoption can improve our inferences about species distributions and the dynamic processes that lead to distributional shifts.

The dilution effect, the hypothesis that biodiversity reduces disease risk, has received support in many systems. However, few dilution effect studies have linked mechanistic experiments to field patterns to establish both causality and ecological relevance. We conducted a series of laboratory experiments and tested the dilution effect hypothesis in an amphibian-Batrachochytrium dendrobatidis (Bd) system and tested for consistency between our laboratory experiments and field patterns of amphibian species richness, host identity and Bd prevalence. In our laboratory experiments, we show that tadpoles can filter feed Bd zoospores and that the degree of suspension feeding was positively associated with their dilution potential. The obligate suspension feeder, Gastrophryne carolinensis, generally diluted the risk of chytridiomycosis for tadpoles of Bufo terrestris and Hyla cinerea, whereas tadpoles of B. terrestris (an obligate benthos feeder) generally amplified infections for the other species. In addition, G. carolinensis reduced Bd abundance on H. cinerea more so in the presence than absence of B. terrestris and B. terrestris amplified Bd abundance on H. cinerea more so in the absence than presence of G. carolinensis. Also, when ignoring species identity, species richness was a significant negative predictor of Bd abundance. In our analysis of field data, the presence of Bufo spp. and Gastrophryne spp. were significant positive and negative predictors of Bd prevalence, respectively, even after controlling for climate, vegetation, anthropogenic factors (human footprint), species richness and sampling effort. These patterns of dilution and amplification supported our laboratory findings, demonstrating that the results are likely ecologically relevant. The results from our laboratory and field data support the dilution effect hypothesis and also suggest that dilution and amplification are predictable based on host traits. Our study is among the first to link manipulative experiments, in which a potential dilution mechanism is supported, with analyses of field data on species richness, host identity, spatial autocorrelation and disease prevalence.

Modern surface wave testing methods rely on advanced signal processing and inversion algorithms to extract information about the shear wave velocity profile from observations of Rayleigh wave propagation at the free surface. Extensive use is made of temporal and spatial Fourier transforms to measure Rayleigh wave dispersion. In this context, it is important to understand the influence of finite temporal and spatial sampling on frequency and wavenumber resolution and aliasing. The aperture smoothing function is shown to be a valuable tool to understand these characteristics for a particular receiver array. Four commonly used methods for calculating dispersion curves — Spectral Analysis of Surface Waves (SASW), Multi-Offset Phase Analysis (MOPA), Spatial Autocorrelation (SPAC), and conventional frequency-domain beamforming — are presented and discussed for both active and passive tests.

Sampling fraction, sample layout, and sampling unit scale are the three basic elements of a spatial sampling scheme. Optimizing these factors plays an important role in decreasing the sampling cost and improving the extrapolation accuracy of survey sampling. Spatial analysis, “3S” techniques, and traditional sampling methods are employed to optimize the three basic elements. Dehui County in Jilin Province, China was chosen as the study area, maize sown acreage as the study object, and square grids as the shape of the sampling units. The experimental results demonstrate that the spatial autocorrelation of sampling unit increases with its scale. When the scale is 500×500 m, there is almost no spatial autocorrelation among sampling units, so 500×500 m is selected as the optimal sampling unit scale. The spatial stratified heterogeneity of sampling units decreases with increasing scale. When the sampling unit scale is 500×500 m, it must be stratified to improve the sampling efficiency. The cultivated land area in one sampling unit can be selected as a stratification criterion due to the significant linear correlation relationship between it and the maize area in all sampling units. Stratified system isometric sampling and 1% are the optimal sample layout pattern and sampling fraction, respectively. This research provides a theoretical basis for improving the spatial sampling efficiency to estimate crop acreage.

Scarabaeus (Pachysoma) comprises 13 flightless dung beetle species endemic to the west coast of southern Africa that are biologically unique and have a variable taxonomic history at the generic level. Initially considered a valid genus Pachysoma was subsequently synonymised with the genus Scarabaeus and later assigned sub-generic status within this genus. In an attempt to resolve past taxonomic inconsistencies, morphological and molecular data partitions, and combinations thereof, were analysed and compared across different phylogenetic inference methods. Trees obtained from the individual datasets which comprised 64 morphological characters and 1197 nucleotide sites of the mitochondrial Cytochrome Oxidase I (COI) gene, were less well-resolved than those obtained from analyses in which the data partitions were combined whilst Bayesian inference generally out-performed parsimony. However, both the individual and combined data partitions, irrespective of the method of analysis, confirmed the monophyly of the Pachysoma lineage. The relative contribution of each data partition to individual nodes as assessed by positive partitioned Bremer support values was 6:5:4 for the molecular:combined:morphological data partitions. However, in terms of contribution to the resolution of terminal versus internal nodes, the morphological dataset made proportionally the greatest contribution and was crucial for recovering a sister relationship of Pachysoma to Scarabaeus, i.e. a generic level, rather than a sub-generic one. This together with the monophyly of the Pachysoma lineage across all methods of analysis and data partitions as well as the unique morphology and biology of its constituent species warrants reinstatement of Pachysoma to its former full generic status.

Fine-scale spatial and temporal dynamics of subtidal reef fish assemblages and implications for monitoring

We investigated the effect of sample size and sampling duration on methane bubble flux (ebullition) estimates from peat using a computer model. A field scale (10 m), seasonal (>100 days) simulation of ebullition from a two‐dimensional (2‐D) structurally varying peat profile was modeled at fine spatial resolution (1 mm × 1 mm). The spatial and temporal scale of this simulation was possible because of the computational efficiency of the reduced‐complexity approach that was implemented, and patterns of simulated ebullition were consistent with those found in the field and laboratory. The simulated ebullition from the peat profile suggested that decreases in peat porosity—which cause increases in gas storage—produce ebullition that becomes increasingly patchy in space and erratic in time. By applying different amounts of spatial and temporal sampling effort, it was possible to determine the uncertainty in ebullition estimates from the peatland. The results suggest that traditional methods to measure ebullition can equally overestimate and underestimate flux by 20% and large ebullition events can lead to large overestimations of flux when sampling effort is low. Our findings support those of field studies, and we recommend that ebullition should be measured frequently (hourly to daily) and at many locations (n > 14).

The thynnine wasp genus Neozeleboria Rohwer is the main pollinating group of the sexually deceptive Australian orchid genus, Chiloglottis R.Br. In a highly specialized interaction, Chiloglottis species attract males from a single or very few Neozeleboria

<title>Abstract</title> Model selection is critical in spatial econometrics, particularly when specifying the degree of spatial dependence in regression models. Traditional criteria such as Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Hannan-Quinn Criterion (HQC) are widely applied but do not adaptively account for the complexity introduced by spatial autocorrelation. This paper evaluates the performance of the Renormalized Maximum Likelihood (RNML) criterion, which incorporates a data-driven penalty derived from the Fisher Information Matrix to balance model fit and complexity. Through Monte Carlo simulations, we assess RNML’s capacity to accurately recover the true spatial dependence structure under varying degrees of spatial autocorrelation and sample sizes. We complement these simulations with three empirical applications: the spatial distribution of GDP per capita across European NUTS-2 regions, the spread of COVID-19 incidence across Italian provinces, and the share of foreign-born residents across Germany's NUTS-3 regions. These examples span diverse spatial scales and policy domains, economic performance, public health, and migration, providing a comprehensive evaluation of RNML in practice. Our findings consistently show that RNML tends to select higher spatial autoregressive coefficients compared to classical criteria, especially in settings with pronounced spatial dependence or higher spatial resolution. This adaptiveness enhances RNML’s reliability for robust inference in spatial modeling, particularly where spatial spillovers and feedback effects are substantively important.

Spatial transcriptomics is an emerging technology widely applied to the analyses of tissue architecture and corresponding biological functions. Substantial computational methods have been developed for analyzing spatial transcriptomics data. These methods generate embeddings from gene expression and spatial locations for spot clustering or tissue architecture segmentation. Although the hyperparameters used to produce an embedding can be tuned for a given training set, a fixed embedding has variable performance from case to case due to data distributions. Therefore, selecting an effective embedding for new data in advance would be useful. For this purpose, we developed an embedding evaluation method named message passing-Moran’s I with maximum filtering (MP-MIM), which combines message passing-based embedding transformation with spatial autocorrelation analysis. We applied a graph convolution to aggregate spatial transcriptomics data and employed global Moran’s I to measure spatial autocorrelation and select the most effective embedding to infer tissue architecture. Sixteen spatial transcriptomics samples generated from the human brain were used to validate our method. The results show that MP-MIM can accurately identify high-quality embeddings that produce a high correlation between the predicted tissue architecture and the ground truth. Overall, our study provides a novel method to select embeddings for new test data and enhance the usability of deep learning tools for spatial transcriptome analyses.

BackgroundEffective monitoring of infectious diseases is crucial for safeguarding public health. Compared to comprehensive nationwide surveillance, selecting representative sample cities to constitute the monitoring network for surveillance provides similar effectiveness at a lower cost. We developed Spatial Cluster Stratified Sampling (SCSS) to select sample cities for infectious diseases exhibiting spatial autocorrelation.MethodsTo improve monitoring efficiency for hand, foot, and mouth disease (HFMD), we used SCSS to design a monitoring network, which involved four main steps. First, we used Spatial Kluster Analysis by Tree Edge Removal (SKATER) to stratify the data. Second, we applied the cost–benefit balance to determine the optimal sample size. Third, we performed simple random sampling within each stratum to establish an initial monitoring network. Fourth, we used cyclic optimization to finalize the monitoring network. We evaluated the spatiotemporal representativeness using root mean square error (RMSE), Spearman's rank correlation, global Moran’s I, local Getis-Ord G*, and Joinpoint Regression. We also compared the effectiveness of SCSS with K-means, traditional stratified sampling, and simple random sampling using RMSE.ResultsThe optimal sample size was determined to be 103. Overall, the predicted values for each city significantly correlated with the true values (r = 0.81, P < 0.001). Both the predicted and true values showed positive spatial autocorrelation (Moran’s I > 0, P < 0.05), and the sensitivity, specificity, and accuracy of the predicted local Getis-Ord G* values, evaluated against the true values as the gold standard, were 0.76, 0.91, and 0.87, respectively. The weekly predicted values for each city showed significant correlation with the true values (P < 0.05). The 95% confidence intervals (CI) for the predicted values of joinpoint locations, annual percent change (APC), and average annual percent change (AAPC) encompassed the true values, and the number of joinpoints matched the true values. Among the four methods compared, SCSS exhibited the lowest and most centralized RMSE.ConclusionsSCSS proved to be more accurate and stable than traditional methods, which overlook spatial information. This method offers a valuable reference for future design of monitoring networks for infectious diseases exhibiting spatial autocorrelation, enabling more efficient and cost-effective surveillance.Graphical Supplementary InformationThe online version contains supplementary material available at 10.1186/s40249-025-01331-7.

Displacement-length scaling relationship for faults: data synthesis and discussion