Spatial autoregressive models for statistical inference from ecological data

Advancements in remote sensing for active fire detection: A review of datasets and methods

This study comprehensively and critically reviews active fire detection advancements in remote sensing from 1975 to the present, focusing on two main perspectives: datasets and corresponding instruments, and detection algorithms. The study highlights the increasing role of machine learning, particularly deep learning techniques, in active fire detection. Looking forward, the review outlines current challenges and future research opportunities in remote sensing for active fire detection. These include exploring data quality management and multi-modal learning, developing spatiotemporally explicit models, investigating self-supervised learning models, improving explainable and interpretable models, integrating physical-process based models with machine learning, and building digital twins to replicate wildfire dynamics and perform what-if scenario analysis. The review aims to serve as a valuable resource for informing natural resource management and enhancing environmental protection efforts through the application of remote sensing technology.

Optimizing spatio-temporal correlation structures for modeling food security in Africa: a simulation-based investigation

This study investigates the impact of spatio- temporal correlation using four spatio-temporal models: Spatio-Temporal Poisson Linear Trend Model (SPLTM), Poisson Temporal Model (TMS), Spatio-Temporal Poisson Anova Model (SPAM), and Spatio-Temporal Poisson Separable Model (STSM) concerning food security and nutrition in Africa. Evaluating model goodness of fit using the Watanabe Akaike Information Criterion (WAIC) and assessing bias through root mean square error and mean absolute error values revealed a consistent monotonic pattern. SPLTM consistently demonstrates a propensity for overestimating food security, while TMS exhibits a diverse bias profile, shifting between overestimation and underestimation based on varying correlation settings. SPAM emerges as a beacon of reliability, showcasing minimal bias and WAIC across diverse scenarios, while STSM consistently underestimates food security, particularly in regions marked by low to moderate spatio-temporal correlation. SPAM consistently outperforms other models, making it a top choice for modeling food security and nutrition dynamics in Africa. This research highlights the impact of spatial and temporal correlations on food security and nutrition patterns and provides guidance for model selection and refinement. Researchers are encouraged to meticulously evaluate the biases and goodness of fit characteristics of models, ensuring their alignment with the specific attributes of their data and research goals. This knowledge empowers researchers to select models that offer reliability and consistency, enhancing the applicability of their findings.

Key predictors of food security and nutrition in Africa: a spatio-temporal model-based study

There is voluminous literature on Food Security in Africa. This study explicitly considers the spatio-temporal factors in addition to the usual FAO-based metrics in modeling and understanding the dynamics of food security and nutrition across the African continent. To better understand the complex trajectory and burden of food insecurity and nutrition in Africa, it is crucial to consider space-time factors when modeling and interpreting food security. The spatio-temporal anova model was found to be superior(employing statistical criteria) to the other three models from the spatio-temporal interaction domain models. The results of the study suggest that dietary supply adequacy, food stability, and consumption status are positively associated with severe food security, while average food supply and environmental factors have negative effects on Food Security and Nutrition. The findings also indicate that severe food insecurity and malnutrition are spatially and temporally correlated across the African continent. Spatio-temporal modeling and spatial mapping are essential components of a comprehensive practice to reduce the burden of severe food insecurity. likewise, any planning and intervention to improve the average food supply and environment to promote sustainable development should be regional instead of one size fit all.

Exposure of protected areas in Central America to extreme weather events

Central America and the Caribbean are regularly battered by megadroughts, heavy rainfall, heat waves, and tropical cyclones. Although 21st-century climate change is expected to increase the frequency, intensity, and duration of these extreme weather events (EWEs), their incidence in regional protected areas (PAs) remains poorly explored. We examined historical and projected EWEs across the region based on 32 metrics that describe distinct dimensions (i.e., intensity, duration, and frequency) of heat waves, cyclones, droughts, and rainfall and compared trends in PAs with trends in unprotected lands. From the early 21st century onward, exposure to EWEs increased across the region, and PAs were predicted to be more exposed to climate extremes than unprotected areas (as shown by autoregressive model coefficients at p < 0.05 significance level). This was particularly true for heat waves, which were projected to have a significantly higher average (tested by Wilcoxon tests at p < 0.01) intensity and duration, and tropical cyclones, which affected PAs more severely in carbon-intensive scenarios. PAs were also predicted to be significantly less exposed to droughts and heavy rainfall than unprotected areas (tested by Wilcoxon tests at p < 0.01). However, droughts that could threaten connectivity between PAs are increasingly common in this region. We estimated that approximately 65% of the study area will experience at least one drought episode that is more intense and longer lasting than previous droughts. Collectively, our results highlight that new conservation strategies adapted to threats associated with EWEs need to be tailored and implemented promptly. Unless urgent action is taken, significant damage may be inflicted on the unique biodiversity of the region.

Melding wildlife surveys to improve conservation inference

Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several datasets. Individually, these datasets may be insufficient for guiding management due to low spatio-temporal resolution, biased sampling, or large observational uncertainty. Integrated models provide an approach for assimilating multiple datasets in a coherent framework that can compensate for these deficiencies. While conventional integrated models have been used to assimilate count data with surveys of survival, fecundity, and harvest, they can also assimilate ecological surveys that have differing spatio-temporal regions and observational uncertainties. Motivated by independent aerial and ground surveys of lesser prairie-chicken, we developed an integrated modeling approach that assimilates density estimates derived from surveys with distinct sources of observational error into a joint framework that provides shared inference on spatio-temporal trends. We model these data using a Bayesian Markov melding approach and apply several data augmentation strategies for efficient sampling. In a simulation study, we show that our integrated model improved predictive performance relative to models for analyzing the surveys independently. We use the integrated model to facilitate prediction of lesser prairie-chicken density at unsampled regions and perform a sensitivity analysis to quantify the inferential cost associated with reduced survey effort.

Latent trajectory models for spatio-temporal dynamics in Alaskan ecosystems

The Alaskan landscape has undergone substantial changes in recent decades, most notably the expansion of shrubs and trees across the Arctic. We developed a Bayesian hierarchical model to quantify the impact of climate change on the structural transformation of ecosystems using remotely sensed imagery. We used latent trajectory processes to model dynamic state probabilities that evolve annually, from which we derived transition probabilities between ecotypes. Our latent trajectory model accommodates temporal irregularity in survey intervals and uses spatio-temporally heterogeneous climate drivers to infer rates of land cover transitions. We characterized multi-scale spatial correlation induced by plot and subplot arrangements in our study system. We also developed a Pólya-Gamma sampling strategy to improve computation. Our model facilitates inference on the response of ecosystems to shifts in the climate and can be used to predict future land cover transitions under various climate scenarios.

A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.

spmodel: Spatial statistical modeling and prediction in [Formula: see text]

spmodel is an [Formula: see text] package used to fit, summarize, and predict for a variety spatial statistical models applied to point-referenced or areal (lattice) data. Parameters are estimated using various methods, including likelihood-based optimization and weighted least squares based on variograms. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable.

An integrated abundance model for estimating county-level prevalence of opioid misuse in Ohio

Opioid misuse is a national epidemic and a significant drug related threat to the United States. While the scale of the problem is undeniable, estimates of the local prevalence of opioid misuse are lacking, despite their importance to policy-making and resource allocation. This is due, in part, to the challenge of directly measuring opioid misuse at a local level. In this paper, we develop a Bayesian hierarchical spatio-temporal abundance model that integrates indirect county-level data on opioid-related outcomes with state-level survey estimates on prevalence of opioid misuse to estimate the latent county-level prevalence and counts of people who misuse opioids. A simulation study shows that our integrated model accurately recovers the latent counts and prevalence. We apply our model to county-level surveillance data on opioid overdose deaths and treatment admissions from the state of Ohio. Our proposed framework can be applied to other applications of small area estimation for hard to reach populations, which is a common occurrence with many health conditions such as those related to illicit behaviors.

Why People Forgo Healthcare in France: A National Survey of 164 092 Individuals to Inform Healthcare Policy-Makers

BACKGROUND

Even in countries having nearly universal healthcare provision some individuals forgo or postpone healthcare to which they are entitled. Socioeconomic and geographic inequalities can make access to healthcare difficult for some people, such that they fail to seek it, particularly if they deem the type of care as non-essential. The need to pay at the point of care, the complexity and cost of top-up health insurance, and delays or only partial reimbursement can discourage take-up of care. This can affect the general health of the population.

METHODS

To estimate the rate of forgoing healthcare in the general French population, between 2015 and 2018 we conducted a nationwide cross-sectional survey of individuals visiting French primary healthcare insurance agencies (Caisse Primaire d'Assurance Maladie, CPAM). We asked whether the person had foregone or postponed healthcare in the last 12 months, if so the types of healthcare forgone or put-off, and reasons. Individuals were stratified by the type of complementary (top-up) health insurance they had.

RESULTS

Out of 164 092 individuals who responded, 158 032 were included in the analysis. Respondents had either private complementary (top-up) insurance (60%), top-up insurance subsidized by the state (29%), or no top-up health insurance (11%). Forgoers (n=40 115; 25.4%) most often lived alone (with or without children), were unemployed, and/ or female. Dental care (54%) and consultations with ophthalmologists, gynaecologists and dermatologists (41%) were most commonly forgone. The reasons were: inability to advance payment and/or to pay the uninsured part (69%), time constraints and difficulty in obtaining appointments (26%).

CONCLUSION

We present a snapshot of forgoing healthcare in a developed country, highlighting the need for continuing review by policy-makers of payment regimens, insurance cover, availability and accessibility. While initiatives have already emerged from the results, further reforms are needed to address the problem of people forgoing preventative or perceived non-urgent healthcare, particularly for disadvantaged subgroups.

EcoCountHelper: an R package and analytical pipeline for the analysis of ecological count data using GLMMs, and a case study of bats in Grand Teton National Park

Here we detail the use of an R package, 'EcoCountHelper', and an associated analytical pipeline aimed at making generalized linear mixed-effects model (GLMM)-based analysis of ecological count data more accessible. We recommend a GLMM-based analysis workflow that allows the user to (1) employ selection of distributional forms (Poisson vs negative binomial) and zero-inflation (ZIP and ZINB, respectively) using AIC and variance-mean plots, (2) examine models for goodness-of-fit using simulated residual diagnostics, (3) interpret model results via easy to understand outputs of changes in predicted responses, and (4) compare the magnitude of predictor variable effects via effects plots. Our package uses a series of easy-to-use functions that can accept both wide- and long-form multi-taxa count data without the need for programming experience. To demonstrate the utility of this approach, we use our package to model acoustic bat activity data relative to multiple landscape characteristics in a protected area (Grand Teton National Park), which is threatened by encroaching disease-white nose syndrome. Global threats to bat conservation such as disease and deforestation have prompted extensive research to better understand bat ecology. Notwithstanding these efforts, managers operating on lands crucial to the persistence of bat populations are often equipped with too little information regarding local bat activity to make informed land-management decisions. In our case study in the Tetons, we found that an increased prevalence of porous buildings increases activity levels of Eptesicus fuscus and Myotis volans; Myotis lucifugus activity decreases as distance to water increases; and Myotis volans activity increases with the amount of forested area. By using GLMMs in tandem with 'EcoCountHelper', managers without advanced programmatic or statistical expertise can assess the effects of landscape characteristics on wildlife in a statistically-robust framework.

A process convolution model for crash count data on a network

Crash data observed on a road network often exhibit spatial correlation due to unobserved effects with inherent spatial correlation following the structure of the road network. It is important to model this spatial correlation while accounting for the road network structure. In this study, we introduce the network process convolution (NPC) model. In this model, the spatial correlation among crash data is captured by a Gaussian Process (GP) approximated through a kernel convolution approach. The GP's covariance function is based on path distance computed between a limited set of knots and crash data points on the road network. The proposed model offers a straightforward approach for predicting crash frequency at unobserved locations where covariates are available, and for interpolating the GP values anywhere on the network. Inference procedure is performed following the Bayesian paradigm and is implemented in R-INLA, which offers an estimation procedure that is very efficient compared to Markov Chain Monte Carlo sampling algorithms. We fitted our model to synthetic data and to crash data from Ottawa, Canada. We compared the proposed approach with a proper Conditional Autoregressive (pCAR) model, and with Poisson Regression (PR) and Negative Binomial (NB) models without latent effects. The results of the study indicated that although the pCAR model has comparable fitting performance, the NPC model outperforms pCAR when the main goal is to predict unobserved locations of interest. The proposed model also offers lower mean absolute error rates for cross validated crash counts, latent variable values, fixed-effect coefficients, as well as shorter interval scores for singletons. The NPC provides a natural way to account for the road network structure when considering the inclusion of spatially structured latent random effects in the modelling of crash data. It also offers an improved predictive capability for crash data on a road network.

Differential equity in access to public and private coastal infrastructure in the Southeastern United States

Despite the ubiquity of coastal infrastructure, it is unclear what factors drive its placement, particularly for water access infrastructure (WAI) that facilitates entry to coastal ecosystems such as docks, piers, and boat landings. The placement of WAI has both ecological and social dimensions, and certain segments of coastal populations may have differential access to water. In this study, we used an environmental justice framework to assess how public and private WAI in South Carolina, USA are distributed with respect to race and income. Using publicly available data from State agencies and the US Census Bureau, we mapped the distribution of these structures across the 301 km of the South Carolina coast. Using spatially explicit analyses with high resolution, we found that census block groups (CBGs) with lower income are more likely to contain public WAI, but racial composition has no effect. Private docks showed the opposite trends, as the abundance of docks is significantly, positively correlated with CBGs that have greater percentages of White residents, while income has no effect. We contend that the racially unequal distribution of docks is likely a consequence of the legacy of Black land loss, especially of waterfront property, throughout the coastal southeast during the past half-century. Knowledge of racially uneven distribution of WAI can guide public policy to rectify this imbalance and support advocacy organizations working to promote public water access. Our work also points to the importance of considering race in ecological research, as the spatial distribution of coastal infrastructure directly affects ecosystems through the structures themselves and regulates which groups access water and what activities they can engage in at those sites.

Laplace approximation for conditional autoregressive models for spatial data of diseases

Conditional autoregressive (CAR) distributions are used to account for spatial autocorrelation in small areal or lattice data to assess the spatial risks of diseases. The intrinsic CAR (ICAR) distribution has been primarily used as the priori distribution of spatially autocorrelated random variables in the framework of Bayesian statistics. The posterior distributions of spatial variates and unknown parameters of Bayesian ICAR models are estimated with the Markov chain Monte Carlo (MCMC) methods or integrated nested Laplace approximation (INLA), which may suffer from failures in numeric convergence. This study used the Laplace approximation, a fast computational method available in software Template Model Builder (TMB), for the maximum likelihood estimation (MLEs) of the ICAR model parameters. This study used the TMB to integrate out the latent spatial variates for the fast computations of marginal likelihood functions. This study compared the runtime and performance among the TMB, MCMC, and INLA implementations with three case studies of human diseases in the United Kingdom and the United States. The MLEs of the ICAR model with TMB were similar to those by the MCMC and INLA methods. The TMB implementation was faster than the MCMC (up to 100–200 times) and INLA (nine times) models.

• This study built conditional autoregressive models in template model builder

• TMB implementation was 100-200 times faster than the MCMC method

• TMB implementation was also faster than Bayesian approximation with R INLA

Spatial frequency of randomly acquired characteristics on outsoles

The highest levels of source association in forensic footwear comparison rely on the agreement between randomly acquired characteristics (RACs) identified on questioned and exemplar test impressions. These features are presumed to be randomly acquired and independent. However, independent acquisition does not necessarily mean these features will be uniformly distributed across an outsole. The aim of this research was to determine if the distribution of RACs in a research dataset could be described by an inhomogeneous Poisson point process based on tread contact and wear. To achieve this goal, RAC spatial frequency from an empirical dataset of shoes was compared against simulated and modeled data assuming a Poisson point process. Deviations in count between the empirical and simulated/modeled predictions were examined using a Poisson rate test and Moran's I. Results indicate that RAC frequency over 67%-79% of an outsole can be reasonably well explained as a Poisson point process or by a Poisson generalized linear regression model (non-spatial GLM) with tread contact as a predictor. Moreover, if the predictor is extended to include both tread contact and wear, RAC counts over 84% of the spatial locations on an outsole are well-explained (although autocorrelation persists). Overall, results indicate that RACs are not uniformly distributed in this dataset, most likely because the factors that dictate RAC development (friction, gait, etc.) are not uniformly distributed. Although this observation in no way negates the use of RACs in forming source associations, the value of a correspondence can differ depending on its spatial location.

Bayesian spatial modeling of COVID-19 case-fatality rate inequalities

The ongoing outbreak of COVID-19 challenges the health systems and epidemiological responses of all countries worldwide. Although preventive measures have been globally considered, the spatial heterogeneity of its effectiveness is evident, underscoring global health inequalities. Using Bayesian-based Markov chain Monte Carlo simulations, we identify the spatial association of socioeconomic factors and the risk for dying from COVID-19 in Colombia. We confirm that from March 16 to October 04, 2020, the COVID-19 case-fatality rate and the multidimensional poverty index have a heterogeneous spatial distribution. Spatial analysis reveals that the risk of dying from COVID-19 increases in regions with a higher proportion of poor people with dwelling (RR 1.74 95%CI = 1.54–9.75), educational (RR 1.69 95%CI = 1.36–5.94), childhood/youth (RR 1.35 95%CI = 1.08–4.03), and health (RR 1.16 95%CI = 1.06–2.04) deprivations. These findings evidence the vulnerability of most disadvantaged members of society to dying in a pandemic and assist the spatial planning of preventive strategies focused on vulnerable communities.

Bayesian Influence Analysis of the Skew-Normal Spatial Autoregression Models

In spatial data analysis, outliers or influential observations have a considerable influence on statistical inference. This paper develops Bayesian influence analysis, including the local influence approach and case influence measures in skew-normal spatial autoregression models (SSARMs). The Bayesian local influence method is proposed to evaluate the impact of small perturbations in data, the distribution of sampling and prior. To measure the extent of different perturbations in SSARMs, the Bayes factor, the ϕ-divergence and the posterior mean distance are established. A Bayesian case influence measure is presented to examine the influence points in SSARMs. The potential influence points in the models are identified by Cook’s posterior mean distance and Cook’s posterior mode distance ϕ-divergence. The Bayesian influence analysis formulation of spatial data is given. Simulation studies and examples verify the effectiveness of the presented methodologies.

Small herbivores and abiotic heterogeneity promote trait variation of a saltmarsh plant in local communities

Intraspecific trait variation (ITV) enables plants to respond to global changes. However, causes for ITV, especially from biotic components such as herbivory, are not well understood. We explored whether small vertebrate herbivores (hares and geese) impact ITV of a dominant clonal plant (Elytrigia atherica) in local communities. Moreover, we looked at the relative importance of their direct (e.g., selective grazing) and indirect effects (altering genotypic richness/diversity and abiotic environment) on ITV. We used exclosures at two successional stages in a Dutch saltmarsh, where grazing pressure at the early successional stage was ca. 1.5 times higher than that of the intermediate successional stage. We measured key functional traits of E. atherica including height, aboveground biomass, flowering (flower or not), specific leaf area, and leaf dry matter content in local communities (1 m × 1 m plots) inside and outside the exclosures. We determined genotypic richness and diversity of each plant using molecular markers. We further measured abiotic variations in topography and clay thickness (a proxy for soil total nitrogen). Structural equation models revealed that small herbivores significantly promoted ITV in height and flowering at the early successional stage, while they marginally promoted ITV in height at the intermediate successional stage. Moreover, the direct effects of herbivores played a major role in promoting ITV. Small herbivores decreased genotypic diversity at the intermediate successional stage, but genotypic richness and diversity did not impact ITV. Small herbivores did not alter topographic variation and variation in clay thickness, but these variations increased ITV in all traits at the early successional stage. Small herbivores may not only impact trait means in plants as studies have shown but also their ITV.

Analysis of the regional distribution of road traffic mortality and associated factors in Japan

BACKGROUND

Regional differences in road traffic (RT) mortality among municipalities have not been revealed in Japan. Further, the association between RT mortality and regional socioeconomic characteristics has not been investigated. We analyzed geographic differences in RT mortality and its associated factors using the Vital Statistics in Japan.

METHODS

We used data on RT mortality by sex and municipality in Japan from 2013 to 2017. We calculated the standardized mortality ratio (SMR) of RT for each municipality by sex using an Empirical Bayes method. The SMRs were mapped onto a map of Japan to show the geographic differences. In addition, an ecological study investigated the municipal characteristics associated with the SMR using demographic socioeconomic, medical, weather, and vehicular characteristics as explanatory variables. The ecological study used a spatial statistical model.

RESULTS

The mapping revealed that the number of municipalities with a high SMR of RT (SMR > 2) was larger in men than in women. In addition, SMRs of capital areas (Kanagawa and Tokyo prefectures) tended to be low in men and women. The regression analysis revealed that population density was negatively associated with the SMR in men and women, and the degree of the association was the largest among explanatory variables. In contrast, there was a positive association between the proportion of non-Japanese persons and SMR. The proportions of lower educational level (elementary school or junior high school graduates), agriculture, forestry, and fisheries workers, service workers, and blue-collar workers were positively associated with the SMR in men. The proportion of unemployed persons was negatively associated with the SMR in men.

CONCLUSIONS

Socioeconomic characteristics are associated with geographic differences in RT mortality particularly in men. The results suggested preventive measures targeted at men of low socioeconomic status and non-Japanese persons are needed to decrease RT mortality further.

Inconsistency of Global Vegetation Dynamics Driven by Climate Change: Evidences from Spatial Regression

Global greening over the past 30 years since 1980s has been confirmed by numerous studies. However, a single-dimensional indicator and non-spatial modelling approaches might exacerbate uncertainties in our understanding of global change. Thus, comprehensive monitoring for vegetation’s various properties and spatially explicit models are required. In this study, we used the newest enhanced vegetation index (EVI) products of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 to detect the inconsistency trend of annual peak and average global vegetation growth using the Mann–Kendall test method. We explored the climatic factors that affect vegetation growth change from 2001 to 2018 using the spatial lag model (SLM), spatial error model (SEM) and geographically weighted regression model (GWR). The results showed that EVImax and EVImean in global vegetated areas consistently showed linear increasing trends during 2001–2018, with the global averaged trend of 0.0022 yr−1 (p < 0.05) and 0.0030 yr−1 (p < 0.05). Greening mainly occurred in the croplands and forests of China, India, North America and Europe, while browning was almost in the grasslands of Brazil and Africa (18.16% vs. 3.08% and 40.73% vs. 2.45%). In addition, 32.47% of the global vegetated area experienced inconsistent trends in EVImax and EVImean. Overall, precipitation and mean temperature had positive impacts on vegetation variation, while potential evapotranspiration and vapour pressure had negative impacts. The GWR revealed that the responses of EVI to climate change were inconsistent in an arid or humid area, in cropland or grassland. Climate change could affect vegetation characteristics by changing plant phenology, consequently rendering the inconsistency between peak and mean greening. In addition, anthropogenic activities, including land cover change and land use management, also could lead to the differences between annual peak and mean vegetation variations.

Improving inferences about private land conservation by accounting for incomplete reporting

Private lands provide key habitat for imperiled species and are core components of function protectected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social-ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.

Association between socio-environmental factors, coverage by family health teams, and rainfall in the spatial distribution of Zika virus infection in the city of Rio de Janeiro, Brazil, in 2015 and 2016

BACKGROUND

Zika virus (ZIKV) infection caused outbreak in Brazil, in 2015 and 2016. Disorganized urban growth, facilitates the concentration of numerous susceptible and infected individuals. It is useful to understand the mechanisms that can favor the increase in ZIKV incidence, such as areas with wide socioeconomic and environmental diversity. Therefore, the study analyzed the spatial distribution of ZIKV in the city of Rio de Janeiro, Brazil, in 2015 and 2016, and associations between the incidence per 1000 inhabitants and socio-environmental factors.

METHODS

The census tracts were used as the analytical units reported ZIKV cases among the city's inhabitants. Local Empirical Bayesian method was used to control the incidence rates' instability effect. The spatial autocorrelation was verified with Moran's Index and local indicators of spatial association (LISA). Spearman correlation matrix was used to indicate possible collinearity. The Ordinary Least Squares (OLS), Spatial Lag Model (SAR), and Spatial Error Model (CAR) were used to analyze the relationship between ZIKV and socio-environmental factors.

RESULTS

The SAR model exhibited the best parameters: R2 = 0.44, Log-likelihood = - 7482, Akaike Information Criterion (AIC) = 14,980. In this model, mean income between 1 and 2 minimum wages was possible risk factors for Zika occurrence in the localities. Household conditions related to adequate water supply and the existence of public sewage disposal were associated with lower ZIKV cumulative incidence, suggesting possible protective factors against the occurrence of ZIKV in the localities. The presence of the Family Health Strategy in the census tracts was positively associated with the ZIKV cumulative incidence. However, the results show that mean income less than 1 minimum wage were negatively associated with higher ZIKV cumulative incidence.

CONCLUSION

The results demonstrate the importance of socio-environmental variables in the dynamics of ZIKV transmission and the relevance for the development of control strategies.

Trait hierarchies are stronger than trait dissimilarities in structuring spatial co-occurrence patterns of common tree species in a subtropical forest

The dissimilarity and hierarchy of trait values that characterize niche and fitness differences, respectively, have been increasingly applied to infer mechanisms driving community assembly and to explain species co-occurrence patterns. Here, we predict that limiting similarity should result in the spatial segregation of functionally similar species, while functionally similar species will be more likely to co-occur either due to environmental filtering or due to competitive exclusion of inferior competitors (hereafter hierarchical competition).We used a fully mapped 50-ha subtropical forest plot in southern China to explore how pairwise spatial associations between saplings and between adult trees were influenced by trait dissimilarity and hierarchy in order to gain insight into assembly mechanisms. We assessed pairwise spatial associations using two summary statistics of spatial point patterns at different spatial scales and compared the effects of trait dissimilarity and trait hierarchy of different functional traits on the interspecific spatial associations. These comparisons allow us to disentangle the effects of limiting similarity, environmental filtering, and hierarchical competition on species co-occurrence.We found that trait dissimilarity was generally negatively related to interspecific spatial associations for both saplings and adult trees across spatial scales, meaning that species with similar trait values were more likely to co-occur and thus supporting environmental filtering or hierarchical competition. We further found that trait hierarchy outweighed trait dissimilarity in structuring pairwise spatial associations, suggesting that hierarchical competition played a more important role in structuring our forest community than environmental filtering across life stages.This study employed a novel method, by offering the integration of pairwise spatial association and trait dissimilarity as well as trait hierarchy, to disentangle the relative importance of multiple assembly mechanisms in structuring co-occurrence patterns, especially the mechanisms of environmental filtering and hierarchical competition, which lead to indistinguishable co-occurrence patterns. This study also reinforced the importance of trait hierarchy rather than trait dissimilarity in driving neighborhood competition.

Modeling the Social and Spatial Proximity of Crime: Domestic and Sexual Violence Across Neighborhoods

OBJECTIVES

Our goal is to understand the social dynamics affecting domestic and sexual violence in urban areas by investigating the role of connections between area nodes, or communities. We use innovative methods adapted from spatial statistics to investigate the importance of social proximity measured based on connectedness pathways between area nodes. In doing so, we seek to extend the standard treatment in the neighborhoods and crime literature of areas like census blocks as independent analytical units or as interdependent primarily due to geographic proximity.

METHODS

In this paper, we develop techniques to incorporate two types of proximity, geographic proximity and commuting proximity in spatial generalized linear mixed models (SGLMM) in order to estimate domestic and sexual violence in Detroit, Michigan and Arlington County, Virginia. Analyses are based on three types of CAR models (the Besag, York, and Mollié (BYM), Leroux, and the sparse SGLMM models) and two types of SAR models (the spatial lag and spatial error models) to examine how results vary with different model assumptions. We use data from local and federal sources such as the Police Data Initiative and American Community Survey.

RESULTS

Analyses show that incorporating information on commuting ties, a non-spatially bounded form of social proximity, to spatial models contributes to better deviance information criteria (DIC) scores (a metric which explicitly accounts for model fit and complexity) in Arlington for sexual and domestic crime as well as overall crime. In Detroit, the fit is improved only for overall crime. The distinctions in model fit are less pronounced when using cross-validated mean absolute error (MAE) as a comparison criteria.

CONCLUSION

Overall, the results indicate variations across crime type, urban contexts, and modeling approaches. Nonetheless, in important contexts, commuting ties among neighborhoods are observed to greatly improve our understanding of urban crime. If such ties contribute to the transfer of norms, social support, resources, and behaviors between places, they may then transfer also the effects of crime prevention efforts.

Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes

We aim to understand the spatial inequality in Coronavirus disease 2019 (COVID-19) positivity rates across New York City (NYC) ZIP codes. Applying Bayesian spatial negative binomial models to a ZIP-code level dataset (N = 177) as of May 31st, 2020, we find that (1) the racial/ethnic minority groups are associated with COVID-19 positivity rates; (2) the percentages of remote workers are negatively associated with positivity rates, whereas older population and household size show a positive association; and (3) while ZIP codes in the Bronx and Queens have higher COVID-19 positivity rates, the strongest spatial effects are clustered in Brooklyn and Manhattan.

Plant Endemism Centres and Biodiversity Hotspots in Greece

Biodiversity hotspots (BH) cover a small fraction of the Earth's surface, yet host numerous endemics. Human-induced biodiversity loss has been increasing worldwide, despite attempts to halt the extinction crisis. There is thus an urgent need to efficiently allocate the available conservation funds in an optimised conservation prioritization scheme. Identifying BH and endemism centres (EC) is therefore a valuable tool in conservation prioritization and planning. Even though Greece is one of the most plant species-rich European countries, few studies have dealt with the identification of BH or EC and none has ever incorporated phylogenetic information or extended to the national scale. Consequently, we are unaware of the extent that Special Areas of Conservation (SAC) of the Natura 2000 network efficiently protect Greek plant diversity. Here, we located for the first time at a national scale and in a phylogenetic framework, the areas serving as BH and EC, and assessed the effectiveness of the Greek SAC in safeguarding them. BH and EC are mainly located near mountainous areas, and in areas supposedly floristically impoverished, such as the central Aegean islands. A critical re-assessment of the Greek SAC might be needed to minimize the extinction risk of the Greek endemics, by focusing the conservation efforts also on the BH and EC that fall outside the established Greek SAC.

What processes must we understand to forecast regional-scale population dynamics?

An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.

Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness

Estimating the effectiveness of protected areas (PAs) in reducing deforestation is useful to support decisions on whether to invest in better management of areas already protected or to create new ones. Statistical matching is commonly used to assess this effectiveness, but spatial autocorrelation and regional differences in protection effectiveness are frequently overlooked. Using Colombia as a case study, we employed statistical matching to account for confounding factors in park location and accounted for for spatial autocorrelation to determine statistical significance. We compared the performance of different matching procedures-ways of generating matching pairs at different scales-in estimating PA effectiveness. Differences in matching procedures affected covariate similarity between matched pairs (balance) and estimates of PA effectiveness in reducing deforestation. Independent matching yielded the greatest balance. On average 95% of variables in each region were balanced with independent matching, whereas 33% of variables were balanced when using the method that performed worst. The best estimates suggested that average deforestation inside protected areas in Colombia was 40% lower than in matched sites. Protection significantly reduced deforestation, but PA effectiveness differed among regions. Protected areas in Caribe were the most effective, whereas those in Orinoco and Pacific were least effective. Our results demonstrate that accounting for spatial autocorrelation and using independent matching for each subset of data is needed to infer the effectiveness of protection in reducing deforestation. Not accounting for spatial autocorrelation can distort the assessment of protection effectiveness, increasing type I and II errors and inflating effect size. Our method allowed improved estimates of protection effectiveness across scales and under different conditions and can be applied to other regions to effectively assess PA performance.

A novel quantitative framework for riverscape genetics

Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To address these challenges, we present a general framework for the design and analysis of riverscape genetic studies. Our framework starts with the estimation of pairwise genetic distance at sample sites and the development of a spatially structured ecological network (SSEN) on which riverscape covariates are measured. We then introduce the novel bidirectional geneflow in riverscapes (BGR) model that uses principles of isolation-by-resistance to quantify the effects of environmental covariates on genetic connectivity, with spatial covariance defined using simultaneous autoregressive models on the SSEN and the generalized Wishart distribution to model pairwise distance matrices arising through a random walk model of geneflow. We highlight the utility of this framework in an analysis of riverscape genetics for brook trout (Salvelinus fontinalis) in north central Pennsylvania, USA. Using the fixation index (F_ST ) as the measure of genetic distance, we estimated the effects of 12 riverscape covariates on geneflow by evaluating the relative support of eight competing BGR models. We then compared the performance of the top-ranked BGR model to results obtained from comparable analyses using multiple regression on distance matrices (MRM) and the program STRUCTURE. We found that the BGR model had more power to detect covariate effects, particularly for variables that were only partial barriers to geneflow and/or uncommon in the riverscape, making it more informative for assessing patterns of population connectivity and identifying threats to species conservation. This case study highlights the utility of our modeling framework over other quantitative methods in riverscape genetics, particularly the ability to rigorously test hypotheses about factors that influence geneflow and probabilistically estimate the effect of riverscape covariates, including stream flow direction. This framework is flexible across taxa and riverine networks, is easily executable, and provides intuitive results that can be used to investigate the likely outcomes of current and future management scenarios.

Ensuring identifiability in hierarchical mixed effects Bayesian models

Ecologists are increasingly familiar with Bayesian statistical modeling and its associated Markov chain Monte Carlo (MCMC) methodology to infer about or to discover interesting effects in data. The complexity of ecological data often suggests implementation of (statistical) models with a commensurately rich structure of effects, including crossed or nested (i.e., hierarchical or multi-level) structures of fixed and/or random effects. Yet, our experience suggests that most ecologists are not familiar with subtle but important problems that often arise with such models and with their implementation in popular software. Of foremost consideration for us is the notion of effect identifiability, which generally concerns how well data, models, or implementation approaches inform about, i.e., identify, quantities of interest. In this paper, we focus on implementation pitfalls that potentially misinform subsequent inference, despite otherwise informative data and models. We illustrate the aforementioned issues using random effects regressions on synthetic data. We show how to diagnose identifiability issues and how to remediate these issues with model reparameterization and computational and/or coding practices in popular software, with a focus on JAGS, OpenBUGS, and Stan. We also show how these solutions can be extended to more complex models involving multiple groups of nested, crossed, additive, or multiplicative effects, for models involving random and/or fixed effects. Finally, we provide example code (JAGS/OpenBUGS and Stan) that practitioners can modify and use for their own applications.

Exploring spatio-temporal patterns in mental health related emergency department use from children and adolescents

To understand the spatio-temporal patterns and associated risk factors with the frequency, we analyze records of mental health related emergency department (MHED) visits from youth. The data are extracted for the period 2002--2011 from the population-based, provincial health administrative data systems of Alberta, Canada. Guided by a descriptive analysis, we conduct generalized linear regression analyses of the counts of MHED visits from various health areas. Seasonal effects are examined via three different types of functions, including trigonometric functions. We specify the temporal correlation using an autoregressive model of order 1 and formulate the spatial correlation by a random effects model. Our analysis reveals a strong seasonal pattern and indicates that the MHED visit counts are significantly associated with age, gender, and a proxy for socio-economic status. The final statistical model may be used to forecast future MHED use and identify regions and groups at a higher risk to the MHEDs.

Spatial Phylogenetics, Biogeographical Patterns and Conservation Implications of the Endemic Flora of Crete (Aegean, Greece) under Climate Change Scenarios

Human-induced biodiversity loss has been accelerating since the industrial revolution. The climate change impacts will severely alter the biodiversity and biogeographical patterns at all scales, leading to biotic homogenization. Due to underfunding, a climate smart, conservation-prioritization scheme is needed to optimize species protection. Spatial phylogenetics enable the identification of endemism centers and provide valuable insights regarding the eco-evolutionary and conservation value, as well as the biogeographical origin of a given area. Many studies exist regarding the conservation prioritization of mainland areas, yet none has assessed how climate change might alter the biodiversity and biogeographical patterns of an island biodiversity hotspot. Thus, we conducted a phylogenetically informed, conservation prioritization study dealing with the effects of climate change on Crete’s plant diversity and biogeographical patterns. Using several macroecological analyses, we identified the current and future endemism centers and assessed the impact of climate change on the biogeographical patterns in Crete. The highlands of Cretan mountains have served as both diversity cradles and museums, due to their stable climate and high topographical heterogeneity, providing important ecosystem services. Historical processes seem to have driven diversification and endemic species distribution in Crete. Due to the changing climate and the subsequent biotic homogenization, Crete’s unique bioregionalization, which strongly reminiscent the spatial configuration of the Pliocene/Pleistocene Cretan paleo-islands, will drastically change. The emergence of the ‘Anthropocene’ era calls for the prioritization of biodiversity-rich areas, serving as mixed-endemism centers, with high overlaps among protected areas and climatic refugia.

Toward Measuring the Level of Spatiotemporal Clustering of Multi-Categorical Geographic Events

Human activity events are often recorded with their geographic locations and temporal stamps, which form spatial patterns of the events during individual time periods. Temporal attributes of these events help us understand the evolution of spatial processes over time. A challenge that researchers still face is that existing methods tend to treat all events as the same when evaluating the spatiotemporal pattern of events that have different properties. This article suggests a method for assessing the level of spatiotemporal clustering or spatiotemporal autocorrelation that may exist in a set of human activity events when they are associated with different categorical attributes. This method extends the Voronoi structure from 2D to 3D and integrates a sliding-window model as an approach to spatiotemporal tessellations of a space-time volume defined by a study area and time period. Furthermore, an index was developed to evaluate the partial spatiotemporal clustering level of one of the two event categories against the other category. The proposed method was applied to simulated data and a real-world dataset as a case study. Experimental results show that the method effectively measures the level of spatiotemporal clustering patterns among human activity events of multiple categories. The method can be applied to the analysis of large volumes of human activity events because of its computational efficiency.

Fuel treatment effectiveness in the context of landform, vegetation, and large, wind-driven wildfires

Large wildfires (>50,000 ha) are becoming increasingly common in semiarid landscapes of the western United States. Although fuel reduction treatments are used to mitigate potential wildfire effects, they can be overwhelmed in wind-driven wildfire events with extreme fire behavior. We evaluated drivers of fire severity and fuel treatment effectiveness in the 2014 Carlton Complex, a record-setting complex of wildfires in north-central Washington State. Across varied topography, vegetation, and distinct fire progressions, we used a combination of simultaneous autoregression (SAR) and random forest (RF) approaches to model drivers of fire severity and evaluated how fuel treatments mitigated fire severity. Predictor variables included fuel treatment type, time since treatment, topographic indices, vegetation and fuels, and weather summarized by progression interval. We found that the two spatial regression methods are generally complementary and are instructive as a combined approach for landscape analyses of fire severity. Simultaneous autoregression improves upon traditional linear models by incorporating information about neighboring pixel burn severity, which avoids type I errors in coefficient estimates and incorrect inferences. Random forest modeling provides a flexible modeling environment capable of capturing complex interactions and nonlinearities while still accounting for spatial autocorrelation through the use of spatially explicit predictor variables. All treatment areas burned with higher proportions of moderate and high-severity fire during early fire progressions, but thin and underburn, underburn only, and past wildfires were more effective than thin-only and thin and pile burn treatments. Treatment units had much greater percentages of unburned and low severity area in later progressions that burned under milder fire weather conditions, and differences between treatments were less pronounced. Our results provide evidence that strategic placement of fuels reduction treatments can effectively reduce localized fire spread and severity even under severe fire weather. During wind-driven fire spread progressions, fuel treatments that were located on leeward slopes tended to have lower fire severity than treatments located on windward slopes. As fire and fuels managers evaluate options for increasing landscape resilience to future climate change and wildfires, strategic placement of fuel treatments may be guided by retrospective studies of past large wildfire events.

Associations of county-level cumulative environmental quality with mortality of chronic obstructive pulmonary disease and mortality of tracheal, bronchus and lung cancers

Chronic obstructive pulmonary disease (COPD) and tracheal, bronchus, and lung (TBL) cancers are among the leading causes of mortality worldwide. Many environmental factors have been linked to COPD and TBL cancers. This study examined the associations of cumulative environmental quality indices with COPD mortality and TBL cancers mortality, respectively. Environmental Quality Index (EQI) was constructed to represent cumulative environmental quality for the overall environment and 5 major environmental domains (e.g., air, water, built). Associations of each EQI indices with COPD mortality and TBL cancers mortality, across 3109 counties in the 48 contiguous states of the US, were examined using simultaneous autoregressive (SAR) models. Stratified analyses were conducted in females versus males and according to rural-urban continuum codes (RUCC) to assess the heterogeneity across the overall population. Overall poor environmental quality was associated with a percent difference (PD) of 0.75 [95% confidence intervals (95% CI), 0.46, 1.05] in COPD mortality and an PD of 1.22 (95% CI, 0.97, 1.46) in TBL cancers mortality. PDs were higher in females than in males for both COPD and TBL cancers. The built domain had the largest effect on COPD mortality (PD, 0.85; 95% CI, 0.58, 1.12) while the air domain had the largest effect on TBL cancers mortality (PD, 1.54; 95% CI, 1.31, 1.76). The EQI-mortality associations varied among different RUCCs, but no consistent trend was found. This result suggests that poor environmental quality, particularly poor air quality and built environment quality may increase the mortality risk for COPD and that for TBL cancers. Females appear to be more susceptible to the effect of cumulative environmental quality. Our findings highlight the importance of improving overall and domain-specific cumulative environmental quality in reducing COPD and TBL cancer mortalities in the United States.

A review of spatial approaches in road safety

Spatial analyses of crashes have been adopted in road safety for decades in order to determine how crashes are affected by neighboring locations, how the influence of parameters varies spatially and which locations warrant interventions more urgently. The aim of the present research is to critically review the existing literature on different spatial approaches through which researchers handle the dimension of space in its various aspects in their studies and analyses. Specifically, the use of different areal unit levels in spatial road safety studies is investigated, different modelling approaches are discussed, and the corresponding study design characteristics are summarized in respective tables including traffic, road environment and area parameters and spatial aggregation approaches. Developments in famous issues in spatial analysis such as the boundary problem, the modifiable areal unit problem and spatial proximity structures are also discussed. Studies focusing on spatially analyzing vulnerable road users are reviewed as well. Regarding spatial models, the application, advantages and disadvantages of various functional/econometric approaches, Bayesian models and machine learning methods are discussed. Based on the reviewed studies, present challenges and future research directions are determined.

Mapping species richness using opportunistic samples: a case study on ground-floor bryophyte species richness in the Belgian province of Limburg

In species richness studies, citizen-science surveys where participants make individual decisions regarding sampling strategies provide a cost-effective approach to collect a large amount of data. However, it is unclear to what extent the bias inherent to opportunistically collected samples may invalidate our inferences. Here, we compare spatial predictions of forest ground-floor bryophyte species richness in Limburg (Belgium), based on crowd- and expert-sourced data, where the latter are collected by adhering to a rigorous geographical randomisation and data collection protocol. We develop a log-Gaussian Cox process model to analyse the opportunistic sampling process of the crowd-sourced data and assess its sampling bias. We then fit two geostatistical Poisson models to both data-sets and compare the parameter estimates and species richness predictions. We find that the citizens had a higher propensity for locations that were close to their homes and environmentally more valuable. The estimated effects of ecological predictors and spatial species richness predictions differ strongly between the two geostatistical models. Unknown inconsistencies in the sampling process, such as unreported observer’s effort, and the lack of a hypothesis-driven study protocol can lead to the occurrence of multiple sources of sampling bias, making it difficult, if not impossible, to provide reliable inferences.

Do multiple environmental factors impact four cancers in women in the contiguous United States?

BACKGROUND

Though there has been an increasing concern about the effects that environmental exposures have on cancer, limited knowledge exists regarding multiple environmental factors on cancers in women.

METHODS

We performed a spatial autoregressive model to examine the association between the Environmental Quality Index (EQI) and mortalities of four cancers in women (breast, cervical, ovarian and uterine cancer) based on county-level data, and explored these associations by urbanicity. The EQI, which included five domains (air, water, land, built environment and sociodemographic domain) estimated from 2000 to 2005 data, was obtained from the United States Environmental Protection Agency. The mortality rates for 3107 counties in the US in 2014 were obtained from the Institute for Health Metrics and Evaluation.

RESULTS

We found that each unit increase in the overall EQI was positively associated with the increased mortality of breast, ovarian and uterine cancer (2.5%, 3.6% and 3.1% respectively), but was negatively associated with cervical cancer mortality. Among the environmental domains, the air and sociodemographic EQIs were positively associated with increased risks of breast, ovarian and uterine cancers. Additionally, built environment EQI was associated with breast and ovarian cancers; land EQI was associated with uterine and ovarian cancers. The sociodemographic EQI was negatively associated with cervical cancer mortality. Furthermore, we have developed a novel Environmental Quality Health Index (EQHI) in identifying environment-health risk of cancers in women at county level.

CONCLUSIONS

Our findings suggest that breast, ovarian, and uterine cancer mortalities are positively associated with multiple environmental factors, while cervical cancer mortality is mainly negatively associated with sociodemographic factors. The novel EQHI might help identify spatially-based environment-cancer risk.