Simultaneous Autoregressive Models Research Articles

Precipitation is the dominant driver for bird species richness, phylogenetic and functional structure in university campuses in northern China

BackgroundAlthough urbanization is threatening biodiversity worldwide, the increasing green urban spaces could harbor relatively high biodiversity. Therefore, how to maintain the biodiversity in urban ecosystem is crucial for sustainable urban planning and management, especially in arid and semiarid regions with relatively fragile environment and low biodiversity. Here, for the first time we linked species richness, phylogenetic and functional structure of bird assemblages in university campuses in northern China with plant species richness, glacial-interglacial climate change, contemporary climate, and anthropogenic factors to compare their relative roles in shaping urban bird diversity.MethodsBird surveys were conducted in 20 university campuses across Inner Mongolia, China. Ordinary least squares models and simultaneous autoregressive models were used to assess the relationships between bird species richness, phylogenetic and functional structure with environmental factors. Structural equation models were used to capture the direct and indirect effects of these factors on the three components of bird diversity.ResultsSingle-variable simultaneous autoregressive models showed that mean annual precipitation was consistently a significant driver for bird species richness, phylogenetic and functional structure. Meanwhile, mean annual temperature and plant species richness were also significant predictors for bird species richness.ConclusionsThis study suggests that campuses with warmer and wetter climate as well as more woody plant species could harbor more bird species. In addition, wetter campuses tended to sustain over-dispersed phylogenetic and functional structure. Our findings emphasize the dominant effect of precipitation on bird diversity distribution in this arid and semiarid region, even in the urban ecosystem.

Finding gaps in TB notifications: spatial analysis of geographical patterns of TB notifications, associations with TB program efforts and social determinants of TB risk in Bangladesh, Nepal and Pakistan

BackgroundIn order to effectively combat Tuberculosis, resources to diagnose and treat TB should be allocated effectively to the areas and population that need them. Although a wealth of subnational data on TB is routinely collected to support local planning, it is often underutilized. Therefore, this study uses spatial analytical techniques and profiling to understand and identify factors underlying spatial variation in TB case notification rates (CNR) in Bangladesh, Nepal and Pakistan for better TB program planning.MethodsSpatial analytical techniques and profiling was used to identify subnational patterns of TB CNRs at the district level in Bangladesh (N = 64, 2015), Nepal (N = 75, 2014) and Pakistan (N = 142, 2015). A multivariable linear regression analysis was performed to assess the association between subnational CNR and demographic and health indicators associated with TB burden and indicators of TB programme efforts. To correct for spatial dependencies of the observations, the residuals of the multivariable models were tested for unexplained spatial autocorrelation. Spatial autocorrelation among the residuals was adjusted for by fitting a simultaneous autoregressive model (SAR).ResultsSpatial clustering of TB CNRs was observed in all three countries. In Bangladesh, TB CNR were found significantly associated with testing rate (0.06%, p < 0.001), test positivity rate (14.44%, p < 0.001), proportion of bacteriologically confirmed cases (− 1.33%, p < 0.001) and population density (4.5*10–3%, p < 0.01). In Nepal, TB CNR were associated with population sex ratio (1.54%, p < 0.01), facility density (− 0.19%, p < 0.05) and treatment success rate (− 3.68%, p < 0.001). Finally, TB CNR in Pakistan were found significantly associated with testing rate (0.08%, p < 0.001), positivity rate (4.29, p < 0.001), proportion of bacteriologically confirmed cases (− 1.45, p < 0.001), vaccination coverage (1.17%, p < 0.001) and facility density (20.41%, p < 0.001).ConclusionSubnational TB CNRs are more likely reflective of TB programme efforts and access to healthcare than TB burden. TB CNRs are better used for monitoring and evaluation of TB control efforts than the TB epidemic. Using spatial analytical techniques and profiling can help identify areas where TB is underreported. Applying these techniques routinely in the surveillance facilitates the use of TB CNRs in program planning.

Beyond taxonomic diversity: Revealing spatial mismatches in phylogenetic and functional diversity facets in Mediterranean tree communities in southern France

Adopting a multifaceted approach of biodiversity is believed to capture different aspects of the ecosystem functioning and it is thus advised for conservation prioritisation, especially for anthropogenic ecosystems but this key topic has never been conducted for the Mediterranean tree assemblages, despite their ecological importance. We explored how the multi-faceted diversity of woody plant assemblages, as measured by taxonomic (TD), functional (FD) and phylogenetic (PD) diversities, are distributed over space in the French part of the Mediterranean biome, and to which extend they are spatially correlated to each other, in order to understand whether one facet can be used as a proxy for another, especially for conservation management purposes.We analysed data from 5885 plots over the study area. We estimated several FD types by considering separately (i) regeneration, (ii) morphology traits (iii) modalities on species ecological properties, and finally (iv) considering all traits together. We used the Rao quadratic entropy to estimate the TD, FD and PD diversity facets. We tested for the links and spatial correlation (congruence) levels among these facets, using simultaneous autoregressive (SAR) models and partial Mantel tests.Spatial structure varied among diversity facets and spatial autocorrelation patterns were identified for all diversity indices from 30 to 50 km distances. We observed a functional convergence and a phylogenetic divergence within tree assemblages comparing to the ones expected given the regional species pool, indicating that even in tree communities with functional similarities, phylogenetic diversity may be high. PD was zero to slightly congruent to FD, regardless the type of functional traits considered. The highest SAR slope (=0.3) and partial Mantel test (=0.2) were revealed between the PD and the FD based on species morphological traits, but still remained considerably low.Each one of the diversity indicators reflected a different tree community spatial pattern. Functional diversity patterns varied according to the type of traits considered. Using only taxonomic indices may be misleading for responding to Mediterranean tree assemblages conservation needs and additional information about the species phylogeny and functional responses to disturbance pressures should be considered in large scale analyses.

High plant species richness and stable climate lead to richer but phylogenetically and functionally clustered avifaunas

AbstractAimsGlacial–interglacial climate stability and contemporary climate could affect bird diversity through their direct effects on diversification and physiological tolerance, and indirect effects on plant species richness, which could provide more food and ecological niches. This study aims to quantitatively assess the direct and indirect effects of these drivers on species richness, phylogenetic and functional structure of bird assemblages.LocationMainland China.TaxonBirds.MethodsOrdinary least squares models and simultaneous autoregressive models were used to test the associations between glacial–interglacial climate anomaly, contemporary climate, plant species richness, elevation range and species richness, phylogenetic structure, functional structure of bird assemblages in mainland China. Structural equation models were used to distinguish between direct and indirect effects of these variables on bird diversity.ResultsHigh bird species richness with phylogenetically and functionally clustered structure occurred in regions with more plant species and stable glacial–interglacial climate. Notably, the phylogenetically clustered bird assemblages were composed of young lineages. Contemporary precipitation and elevation range affected bird diversity through their indirect effects on plant species richness.Main ConclusionsThese findings suggest that more plant species and stable glacial–interglacial climate may promote bird diversity through their effects on in situ diversification and species dispersal rate in southwestern China. Therefore, the well‐preserved plant and bird diversity through the long‐term climate fluctuations should be of great importance of biodiversity conservation in China.

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 (FST ) 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.

More endemic birds occur in regions with stable climate, more plant species and high altitudinal range in China

BackgroundGeographical distribution of endemic species and its multiple scale drivers are an important topic in biodiversity conservation, because these species are especially vulnerable to climate change and habitat degradation, and therefore have high conservation priority. Here, for the first time, we simultaneously linked glacial-interglacial climate change, contemporary climate, plant species richness and altitudinal range with endemic bird distribution in China.MethodsOrdinary least squares models and simultaneous autoregressive models were used to assess the associations between endemic bird richness, endemic bird ratio and each explanatory variable. Structural Equation Models were also performed to test the direct and indirect effects of these variables on endemic bird richness and endemic bird ratio.ResultsHigher endemic bird richness and endemic bird ratio occurred in regions with stable glacial-interglacial temperature, i.e., southwest China. Plant species richness and altitudinal range were also positively correlated with endemic bird richness and endemic bird ratio. Glacial-interglacial temperature change, contemporary precipitation, plant species richness and altitudinal range were all included in the best combination of variables for endemic bird richness. Importantly, glacial-interglacial temperature change had strong direct effects on both endemic bird richness and endemic bird ratio, while plant species richness only had a direct effect on endemic bird richness.ConclusionsOur results indicate that endemic birds in China concentrate in southwest regions where there are stable glacial-interglacial temperature, more plant species and larger altitudinal range. Notably, while stable climate has strong direct effects on both endemic bird richness and endemic bird ratio, plant species may affect endemic bird richness through its effect on overall bird species richness. Importantly, the recent anthropogenic activities have also significantly intensified in this region, which would pose huge challenge for biodiversity conservation in China.

Forest canopy height co‐determines taxonomic and functional richness, but not functional dispersion of mammals and birds globally

AbstractAimsTaller forest canopies may harbour higher biodiversity by providing more and varied resources. No previous studies have assessed whether forest canopy height shapes the taxonomic and functional diversity of terrestrial vertebrates at global and regional scales. Here, we examine the roles of forest canopy height and other environmental variables in shaping global and regional patterns of species richness and functional diversity of mammals and birds.LocationGlobal.Time periodPresent day.Major taxa studiedTerrestrial mammals and birds.MethodsGlobal forest canopy height data at 1 km spatial resolution were used to measure forest vertical structure. Species richness, functional richness and functional dispersion of mammals and birds were calculated using range maps and trait data. Spatial simultaneous autoregressive error models were used to evaluate associations between species richness and functional diversity and their predictors, including mean canopy height, standard deviation of canopy height, net primary productivity, current climate and historical climate stability, topography and human activities.ResultsMean canopy height emerged as one of two predictors most associated with species richness of mammals and birds as well as mammal functional richness. However, mean canopy height had little explanatory power for functional dispersion. Mean annual temperature and net primary productivity contributed most to explain global‐scale mammal and bird functional dispersion. At the regional scale, mean canopy height, net primary productivity and mean annual temperature were the variables most associated with the species richness and functional diversity of mammals and birds.Main conclusionsForest canopy height is an important predictor of species richness and functional diversity of terrestrial vertebrates at both global and regional scales, at a similar overall level to productivity and temperature. Our study highlights the crucial role of the complex vertical structure in shaping the global and regional patterns of vertebrate diversity.

Spatial Regression with Multiple Dependent Variables: Principal Component Analysis and Spatial Autocorrelation

Simultaneous studies of multiple health conditions over geographic areas can be enhanced by the principal component analysis (PCA). However, the presence of spatial autocorrelation may induce nonlinearity that compromises PCA. This article presents an approach that combines the residual standardization method in PCA with a spatial regression method to account for spatial autocorrelation. It first estimates a multivariate simultaneous autoregressive model for parameter estimates and residuals. It then uses the residuals to formulate a standardized matrix for singular value decomposition. Simulation in various scenarios demonstrates that the proposed approach can effectively remove spatial dependent effects between spatial units to capture pure correlation effects within spatial units. Two case studies examine hospitalization and cancer data in Nebraska. The first demonstrates a way to account for spatial dependency in 39 hospitalization conditions over 156 census tracts. The second applies regression residuals to PCA to evaluate potentially elevated cancer risks near two nuclear power plants. The results show that accounting for spatial dependency and explanatory variables can help reveal information about multiple health outcomes.

High correlation of species diversity patterns between specialist herbivorous insects and their specific hosts

AbstractAimThe relative importance of biotic and abiotic factors in shaping the species diversity of temperate organisms has been debated for a long time. Here, we used drepanosiphine aphids as a model group to validate the ‘resource‐dependent hypothesis’ based on the biotic interactions for specialist herbivorous insects, a hypothesis stating that their intricate relationship with host plants for resources might constrain their species diversity from being positively correlated with the species diversity of their hosts, and to assess the role of biotic and abiotic factors in shaping the global species diversity pattern of temperate organisms.LocationGlobal and regional.Time periodCurrent.TaxonDrepanosiphine aphids (Insecta: Hemiptera: Aphidoidea).MethodsWe first investigated the global species diversity pattern of drepanosiphine aphids. Then we used ordinary least squares (OLS) and spatial simultaneous autoregressive (SAR) models to conduct regression analyses to test for the relationships of global and regional species richness between drepanosiphine aphids and their host plants, and other predictor variables describing entire woody plants, environmental energy, water availability, climate seasonality, topographical heterogeneity and land cover. Finally, structural equation models (SEMs) were further used to test for direct and indirect effects of key ecological variables.ResultsThe distribution hotspots of species richness and endemism were both located in temperate zones, supporting the notion that drepanosiphine aphids were representative of temperate organisms. The specific host plant was the most important ecological variable in explaining global and regional drepanosiphine species richness, showing a strong positive relationship. The climatic variables mainly showed an indirect effect on global and regional drepanosiphine species richness via specific hosts.Main ConclusionsThe global and regional drepanosiphine species richness was primarily shaped by specific host plants as opposed to all woody trees, with indirect effects of climatic factors via host plants, which supported the ‘resource‐dependent hypothesis’ based on the biotic interactions for specialist herbivorous insects. This finding sheds new light on the ‘biotic interactions hypothesis’ that states biotic interactions can primarily affect the distributions and diversity patterns of both tropical and temperate organisms not only at the local scale but also at the regional to global scales.

Likelihood-based strategies for estimating unknown parameters and predicting missing data in the simultaneous autoregressive model

We attempt a three-stage comparison of several strategies for estimating parameters and predicting data in the simultaneous autoregressive model, which is a regression model with spatial autocorrelation in the disturbance between locations as the unit of observation. These strategies differ according to the formulation of the log-likelihood function containing a parametric weight matrix. In the first stage, a chain of logical reasoning is used to obtain theoretical findings by assuming that the data generating model and the data fitting model coincide. We consider the possibility that a subset of locations may be included in neither the parameter estimation nor the data prediction. In the second stage, a series of Monte Carlo experiments are conducted to supplement the theoretical comparison by considering also a mismatch between the two models. The prevalent strategy is defined as an approach that is not based on the exact log-likelihood function, regardless of the setting. The use of this strategy indicates that the parameter estimators do not reflect the mutual connection between all the locations included in the prediction. In the third stage, an empirical comparison is made to confirm the findings from the experimental comparison by using data observed in the real world. We conclude that the reasonable choice is not the prevalent strategy, but a strategy that can be defined as an approach based on the exact log-likelihood function, depending on the setting. The reasonable strategy tailors the parameter estimators to suit the mutual connection between all the locations included in the prediction.

Geographical patterns and current and short‐term historical correlates of phylogenetic diversity and endemism for New World primates

AbstractAimAddressing the phylogenetic relationship among species has been insightful to understand evolutionary and biogeographical processes shaping the distribution of diversity across broad geographical gradients. Here we determine the geographical distribution and the variables correlated with the phylogenetic component of diversity and endemism for primates, as well as their relationships with the taxonomic component of diversity.LocationNew World.TaxonNew World monkeys, Platyrrhini.MethodsWe mapped primate occurrence in cells of 110 × 110 km. For each cell, we calculated phylogenetic diversity (PD) and endemism (PE), as well as how much these indices deviate from their expected values based on species richness (SR) and endemism (SE), respectively. Using simultaneous autoregressive models, we tested the effects of mean annual temperature, forest structure and climate history on the current patterns of primate phylogenetic diversity.ResultsWe found that most primate phylogenetic diversity is found in the tropical forests of South America. Tropical forests also present much higher PD and PE than predicted by SR and SE, respectively, demonstrating their importance in producing and maintaining primate phylogenetic diversity in the New World. Furthermore, regions presenting low SR and SE nevertheless show high PE, likely because these areas have witnessed the evolution of many clades occurring exclusively therein. Overall, mean annual temperature had the highest influence in explaining phylogenetic indices.Main conclusionsOur results suggest a strong influence of primate biogeography throughout the New World on the geographical distribution of the phylogenetic component of their diversity, with the Amazon, Atlantic Forest and Central America containing the greatest amount and endemism of phylogenetic diversity. Our study contributes to a better understanding of the dynamics underlying the geographical distribution of New World's primate diversity, especially in a phylogenetic context in which their biogeographical history has deeply affected current patterns of primate diversity in this region. Our results provide essential information for the conservation of primate evolutionary history, especially considering the forecast effects of habitat loss and climate change on the dynamics of tropical forests.

Previous year outbreak conditions and spring climate predict spruce budworm population changes in the following year

We determined effects of local spruce budworm (Choristoneura fumiferana Clem.; SBW) population level, proximity to sites with high SBW populations, insecticide spray, and environmental variables on SBW populations from 2014 to 2018, the outbreak initiation period in northern New Brunswick, Canada. SBW second instar larvae (L2) per branch data collected at 1100–2000 sample points per year were used to create annual interpolated population rasters. Fishnet sample points extracted from these rasters were overlaid with georeferenced layers of 46 possible predictor variables including forest composition, climate, topography, site quality, and insecticide treatment. Results showed that local SBW population in the previous year, proximity to sites with high SBW populations, and early spring climate were consistently the most important predictors over the 5 study years. Simultaneous autoregressive models were used to address spatial autocorrelation when forecasting the SBW L2 population, and a linear mixed effects model was fit to aggregate data for 2015–2018. The models reduced spatial dependence in the residuals, and explained 68–79% of variance in annual L2 levels and 53% of variance over the 4 years combined. Sensitivity analysis showed that locations with 5–10 more SBW L2 per branch than observed values, or 20–40 km closer to high population sites in the previous year could have up to 24 more L2 in the current year. Cumulative degree days in April helped to estimate the upper and lower bounds of the population. Expansion and retraction of SBW outbreak initiation were mathematically described. Understanding which variables influence SBW outbreak initiation and population level assists in design of small area target-specific insecticide spray applications and helps focus SBW L2 sampling on predicted outbreak hot spots.

Predicting landscape-scale summer resource selection for the northern long-eared bat (Myotis septentrionalis) in Iowa

AbstractThe northern long-eared bat (Myotis septentrionalis) is currently listed as threatened under the U.S. Endangered Species Act largely due to population declines resulting from the spread of white-nose syndrome in North America. White-nose syndrome was confirmed in Iowa in 2015, emphasizing a need to closely monitor populations of M. septentrionalis statewide. We applied presence-only models to predict landscape-scale resource selection by M. septentrionalis using roost tree observations and mist net captures from various research and environmental assessment projects in Iowa (2003–2015). We used a simultaneous autoregressive (SAR) model to account for residual spatial autocorrelation in our compiled data set and estimate the proportional probability of use of summer habitats for M. septentrionalis. We estimated SAR models using four environmental predictor variables measured at two landscape scales (0.5- and 2.4-km) representative of M. septentrionalis home range sizes in North America. The SAR models resulted in high predictive fit with withheld test observations and an independent data set of acoustic detections of M. septentrionalis from recent surveys (2016–2018), indicating a significant positive relationship existed between habitat quality (as an index of selection) and distribution of M. septentrionalis at landscape scales. At both spatial scales, M. septentrionalis showed positive selection of closed canopy interior forest, bottomland hardwood forest, and total perennial stream length, whereas at the 0.5-km scale, M. septentrionalis also showed a positive association with open canopy forest. Our models indicated that up to 7.0% and 8.5% of the state was comprised of potentially suitable forested summer habitats for M. septentrionalis for 0.5- and 2.4-km scales, respectively. Our models also indicated the distribution of highly selected habitats at landscape scales in Iowa and accurately predicted independent observations of M. septentrionalis in areas of the state where no capture efforts have occurred. This study provides methods to predict landscape-scale resource selection and distribution for bats where multiple fine-scale data sources exist across broad geographic regions.

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.

Effect of spatially correlated initial geometric imperfection on reliability of spherical latticed shell considering global instability

Latticed shells are extensively used for large-span structures because of their good structural behaviour, lightweight and ease of construction. Their codified design is often governed by the global instability requirement for gravity load and specified shape and amplitude of maximum initial geometric imperfection (IGI). The reliability basis for the assigned shape and maximum IGI is unknown. In the present study, it is proposed that IGI is modelled using the simultaneous autoregressive model. It is shown that this model is flexible in modelling spatially varying correlated imperfection and can cope with irregular grid system. By using this model, the effect of the spatially correlated IGI on the probability distribution of the load capacity for global instability and the reliability of spherical latticed shell designed according to the Chinese design code is assessed. It is shown that the use of the prefixed shape of the IGI to assess the probability distribution of the load capacity is unconservative as compared to that by using spatially varying random imperfection. The shape of the probability distribution for the former differs from that for the latter. The reliability analysis results indicate that the use of a critical load factor of 2 implemented in the code leads to a failure probability less than about 10−6 for global instability if the specified maximum IGI defined in the code equals about two to three times the standard deviation of IGI. This failure probability becomes less than about 10−5 if a critical load factor of 1.5 is used for design. It is suggested that the code recommended critical load factor may be reduced to 1.5 while achieving target reliability index often adopted for design code calibration.

Modeling multiscalar influences on natural hazards vulnerability: a proof of concept using coastal hazards in Sarasota County, Florida

Quantitative vulnerability modeling provides local decision makers and planners with tangible scores that they can utilize to develop and implement effective mitigation strategies. Considering appropriate scale and quantitative methods in vulnerability assessments can help decision makers develop hazard mitigation policies that are effective at the jurisdictional level at which they are implemented. Several types of statistical approaches such as principal component analysis, classical regression and simultaneous autoregressive models have been used to measure vulnerability at various scales (e.g. state or county), but these approaches have limitations for measuring sub-county vulnerability. This paper discusses existing statistical methods utilized in vulnerability assessments and presents a hierarchical generalized linear regression model (Hierarchical GLM) with multiscalar indicators and spatial components. Our model results illustrate that certain indicators are spatially and scalar dependent, providing evidence that examining vulnerability at a sub-county scale while also maintaining a multiscalar perspective offers additional information about social-environmental vulnerability than provided by typical approaches.

Distant political‐economic forces and global‐to‐local pathway to impacts on forests of Ejido landscapes across Yucatán, México

AbstractIndigenous community land tenure in many locations worldwide is shifting towards individually parcelized and privatized systems. Among the drivers of this shifting land tenure are distant political‐economic forces and commodity markets, from local to global. Accompanying the observed land tenure changes are shifts in livelihoods, away from subsistence‐based and toward market‐oriented activities. These changes can ultimately impact land use, land cover, and biodiversity conservation. We investigated a global‐to‐local causal pathway, from agriculture, livestock, and forestry production for distant markets, extending through shifting land tenure and livelihoods, to impacts on forest cover within ejidos (a type of community landholding) across Yucatán, México, where Maya people are the primary land managers. To reveal this causal pathway, we conducted exploratory data analysis, using ordinary least squares regression, mapped variables, and variographic analyses to assess spatial patterns and correlations. We further explored relationships among variables using spatially explicit simultaneous autoregressive models. We found that commodity production for distant markets is strongly related to parcelized ejido lands, which in turn are often deforested. Conversely, community‐managed lands, which traditionally involve subsistence‐based agroforestry, are much more likely to be densely forested. Overall, we conclude that recent deforestation of ejido lands across the State is, at least partly, the result of shifting land tenure and livelihoods due to the increasing presence of commodity markets. Moreover, we conclude that community‐managed lands and associated subsistence livelihoods can attenuate deforestation and potentially advance forest and biodiversity conservation across México and elsewhere.

Nonparametric Bayesian analysis of Korean elderly suicide rates from small areas

Disease mapping is an area of epidemiology concerned with estimating the spatial pattern in disease risk over a geographical region. Typically, the study area is partitioned into spatial districts (small areas), with the disease map displaying the disease incidence associated with each district. We consider Bayesian nonparametric small-area models with a Dirichlet process and a simultaneous conditional autoregressive model for the spatial correlation between neighboring areas to estimate the standardized mortality ratio and mapping. The observed mortality rates are unstable for regions with rare events such as cancer. Bayesian methods, similar to the proposed small-area analysis method, provide mechanisms for smoothing the estimates of rates by region. We compare nonparametric Bayesian models using a Dirichlet process prior with the parametric Bayesian models using a gamma prior for small-area estimation.

Species and phylogenetic endemism in angiosperm trees across the Northern Hemisphere are jointly shaped by modern climate and glacial–interglacial climate change

AbstractAimsPhylogenetic endemism describes the extent to which unique phylogenetic lineages are constrained to restricted geographic areas. Previous studies indicate that species endemism is related to both past and modern climate, but studies of phylogenetic endemism are relatively rare and mainly focused on smaller regions. Here, we provide the first assessment of the patterns of species and phylogenetic endemism in angiosperm trees across the Northern Hemisphere as well as the relative importance of modern climate and glacial–interglacial climate change as drivers of these patterns.LocationNorthern Hemisphere.Major taxaAngiosperm trees.MethodsUsing tree assemblages at the scale of 100 km × 100 km grid cells and simultaneous autoregressive (SAR) models, we assessed the relationships between species endemism, phylogenetic endemism and modern climate variables, Last Glacial Maximum (LGM) to present temperature velocity.ResultsSpecies and phylogenetic endemism were associated with both modern climate and glacial–interglacial climate change, with higher values in areas with stable historical climate and warmer and wetter modern conditions. Notably, the multivariate SAR analyses showed that the combinations of variables with highest Akaike’s information criterion (AIC) weight always included both LGM–present climate instability and modern climate, that is, modern precipitation and temperature.Main conclusionsOur results show that high phylogenetic endemism is partially dependent on long‐term climate stability, highlighting the threat posed by future climate changes to the preservation of rare, phylogenetically distinct lineages of trees.

Zoogeographic patterns of pelagic oceanic cephalopods along the eastern Pacific Ocean

AbstractAimTo analyse the diversity and distribution of oceanic pelagic cephalopods along the eastern Pacific Ocean assessing the existence of biogeographic structuring, and the role of physical variables in generating geographical patterns. We hypothesized that the control by environmental factors, and the effect of geometric constraints, determine the range size and limits of distribution of oceanic cephalopods along the eastern Pacific Ocean, generating a latitudinal gradient in species richness.LocationEastern Pacific Ocean (60°N – 60°S), from the Gulf of Alaska to the Southern Ocean.MethodsBased on a literature review and &gt;5,000 records obtained from collections, we constructed a presence–absence matrix including 61 latitudinal bands (2° each) along the Eastern Pacific, and estimated species richness and range endpoints at each band. Biogeographic units were determined by means of multivariate analyses. Species richness was compared with null model predictions in order to test for the existence of geometric constraints using the Mid‐Domain Null model. The effects on species richness of environmental variables (temperature, salinity and oxygen) were evaluated separately for surface and depth (0–1,000 m) data, by means of ordinary least squares regression and simultaneous autoregressive models. Rapoport's pattern was assessed by applying the Stevens’ method and the range midpoint method.ResultsSpecies richness was high across the tropics and decreased towards the both poles. We identified five biogeographic units, highlighting two major distribution breaks at 40°N and 42°S. Species richness was strongly related with environmental variables, although the combined variables accounted for a large fraction of the variance between 0 and 1,000 m (R2 = 0.99), while temperature was the best single predictor at the surface (R2 = 0.98). Species richness curves showed a mid‐domain effect (MDE), and the mean latitudinal range was higher in the tropics and at warm latitudes, generating an inverse Rapoport's pattern.Main conclusionsAlong the eastern Pacific Ocean oceanic cephalopods exhibit both a clear biogeographic patterning and an interhemispheric (poleward decreasing) diversity gradient, which appear strongly related with physical factors and external forcing, as well as with a MDE as a seemingly consequence of the naturally bounded domain of the Eastern Pacific.