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Many statistical models currently used in ecology and evolution account for covariances among random errors. Here, I address five points: (i) correlated random errors unite many types of statistical models, including spatial, phylogenetic and time‐series models; (ii) random errors are neither unpredictable nor mistakes; (iii) diagnostics for correlated random errors are not useful, but simulations are; (iv) model predictions can be made with random errors; and (v) can random errors be causal?These five points are illustrated by applying statistical models to analyse simulated spatial, phylogenetic and time‐series data. These three simulation studies are paired with three types of predictions that can be made using information from covariances among random errors: predictions for goodness‐of‐fit, interpolation, and forecasting.In the simulation studies, models incorporating covariances among random errors improve inference about the relationship between dependent and independent variables. They also imply the existence of unmeasured variables that generate the covariances among random errors. Understanding the covariances among random errors gives information about possible processes underlying the data.Random errors are caused by something. Therefore, to extract full information from data, covariances among random errors should not just be included in statistical models; they should also be studied in their own right. Data are hard won, and appropriate statistical analyses can make the most of them. [ABSTRACT FROM AUTHOR]

Binary outcome models are frequently used in the social sciences and economics. However, such models are difficult to estimate with interdependent data structures, including spatial, temporal, and spatio-temporal autocorrelation because jointly determined error terms in the reduced-form specification are generally analytically intractable. To deal with this problem, simulation-based approaches have been proposed. However, these approaches (i) are computationally intensive and impractical for sizable datasets commonly used in contemporary research, and (ii) rarely address temporal interdependence. As a way forward, we demonstrate how to reduce the computational burden significantly by (i) introducing analytically-tractable pseudo maximum likelihood estimators for latent binary choice models that exhibit interdependence across space and time and by (ii) proposing an implementation strategy that increases computational efficiency considerably. Monte Carlo experiments show that our estimators recover the parameter values as good as commonly used estimation alternatives and require only a fraction of the computational cost. [ABSTRACT FROM AUTHOR]

Research about urban local climate and urban heat island often relies on land surface temperature (LST) data to characterize the distribution of temperature near the surface. Although using remotely sensed data for such work has the advantage of continuous spatial coverage at regular temporal intervals, it is recognized that surface temperature is not an ideal proxy for air temperature (AT). This study's goal is to develop a spatiotemporal model revealing the relationship between LST and AT within the complexities of the urban environment. A mobile weather monitoring unit was used to collect spatially-explicit fine-scale AT data while Landsat 8 and 9 passed overhead collecting LST data. A spatiotemporal model of the relationship between LST and AT in Philadelphia was constructed with this data utilizing basis functions to account for spatial and temporal autocorrelation. The spatiotemporal model results show a strong relationship between LST and AT and indicate that it is possible to predict fine scale AT (120 m) using remotely sensed LST in an urban context (r-squared = 0.99, RMSE = 0.89 °C). The spatiotemporal model outperforms models that do not account for spatial and temporal autocorrelation, highlighting the importance of considering these dependencies in temperature modeling. City-wide AT predictions were generated for Philadelphia demonstrating the ability of the model to improve understanding of local urban climate. • LST data is often used as a proxy for AT data in studies of urban climate. • Access to AT data is a barrier to modeling efforts. • Mobile monitoring can be used to collect large amounts of AT data. • LST data can be used to predict spatially fine-scale AT in urban environments. • Spatial and temporal autocorrelation must be considered in AT modeling efforts. [ABSTRACT FROM AUTHOR]

Objective: This article introduces Bayesian spatial–temporal modeling for social and health science research. We use the World Bank's World Development Indicators data on youth unemployment and HIV risk in Africa to illustrate the utility of the Bayesian paradigm in modeling space–time changes in outcomes. Method: Data on adolescents and young adults were collected in 36 African countries from 1991 to 2014. We examined associations between HIV risk and youth unemployment rates using 16 Bayesian Poisson models incorporating spatial and temporal autocorrelations. Results: The best fit to the data was the model with spatially uncorrelated heterogeneity, temporally correlated random-walk autocorrelation, and spatial–temporal interaction. HIV risk factors are spatially uncorrelated across 36 countries but temporally correlated (i.e., country and time interaction) over the data collection period. The relationship between HIV risk and unemployment rate is statistically nonsignificant because of large spatial–temporal variations. Conclusions: This article demonstrates the capacity of Bayesian modeling to incorporate spatial (neighborhood) and temporal (historical) information to reflect not only the influences of space and time but also their interactions on the phenomenon of interest. The Bayesian framework holds great promise for improving the dynamic targeting of interventions and strategies to achieve desired outcomes. [ABSTRACT FROM AUTHOR]

Site occupancy‐detection models (SODMs) are statistical models widely used for biodiversity surveys where imperfect detection of species occurs. For instance, SODMs are increasingly used to analyse environmental DNA (eDNA) data, taking into account the occurrence of both false‐positive and false‐negative errors. However, species occurrence data are often characterized by spatial and temporal autocorrelation, which might challenge the use of standard SODMs. Here we reviewed the literature of eDNA biodiversity surveys and found that most of studies do not take into account spatial or temporal autocorrelation. We then demonstrated how the analysis of data with spatial or temporal autocorrelation can be improved by using a conditionally autoregressive SODM, and show its application to environmental DNA data. We tested the autoregressive model on both simulated and real data sets, including chronosequences with different degrees of autocorrelation, and a spatial data set on a virtual landscape. Analyses of simulated data showed that autoregressive SODMs perform better than traditional SODMs in the estimation of key parameters such as true‐/false‐positive rates and show a better discrimination capacity (e.g., higher true skill statistics). The usefulness of autoregressive SODMs was particularly high in data sets with strong autocorrelation. When applied to real eDNA data sets (eDNA from lake sediment cores and freshwater), autoregressive SODM provided more precise estimation of true‐/false‐positive rates, resulting in more reasonable inference of occupancy states. Our results suggest that analyses of occurrence data, such as many applications of eDNA, can be largely improved by applying conditionally autoregressive specifications to SODMs. [ABSTRACT FROM AUTHOR]

We assessed the effect of survey design on the results when conducting fruit body surveys of wood-inhabiting fungi. Our results demonstrate that the optimal design depends on the ecological question to be addressed, as well as the group of fungal species under research. If the aim is to record the total species richness in a dead wood unit or to estimate the population size of a species, repeating the survey over time is generally necessary. However, if the aim is to estimate the total species richness in the forest or to assess how environmental covariates influence species richness or community composition, it is generally more efficient to increase the number of dead wood units than to re-survey the same ones. Among the morphological fungal groups, the results of agarics improved the most and of polypores and corticioids the least with repeating surveys over time. [ABSTRACT FROM AUTHOR]

Tropical timber production forests have the potential to harbour many species, but landscape-level management strategies for minimizing extinctions are lacking., To develop relevant conservation guidelines, we investigated how harvest plans with different spatiotemporal patterns affected the persistence of sessile species with different traits and preharvest characteristics. We confronted this problem via an individual-based simulation model., We explored several harvest plans with different levels of spatiotemporal aggregation, but equivalent total harvest area, and found that extinction probabilities for each plan varied with species traits and predisturbance characteristics. Most notably, plans with large contiguous harvest units yielded particularly high extinction probabilities for dispersal-limited habitat specialists with clustered preharvest distributions. Differences between plans were small for some types of species, but highly aggregated plans yielded the highest extinction rates for all extinction-prone groups., Synthesis and applications. Our simulations suggest that reducing the size of contiguous harvest units, even while total harvest area remains constant, may reduce extinction rates in tropical production forests (assuming that road-related threats can be effectively managed). Our findings have important implications for tropical conservation efforts and also provide general insight into the compositional effects of disturbances with different spatiotemporal characteristics. [ABSTRACT FROM AUTHOR]

The article investigates the connection of spatial distribution, climate and soil moisture-holding capacity in plague epizootics in black-tailed prairie dogs, Cynomys ludovicianus, in northeastern Colorado. The study uses regression model that was parameterized using data from 1981 to 2003 and validated with data from 2004 to 2005. The study suggests that plague occurrence depends upon direct climatic effects on flea vectors and the plague pathogen.

Global change analyses are facilitated by the growing number of remote-sensing datasets that have both broad spatial extent and repeated observations over decades. These datasets provide unprecedented power to detect patterns of time trends involving information from all pixels on a map. However, rigorously testing for time trends requires a solid statistical foundation to identify underlying patterns and test hypotheses. Appropriate statistical analyses are challenging because environmental data often have temporal and spatial autocorrelation, which can either obscure underlying patterns in the data or suggest false associations between patterns in the data and independent values used to explain them. Existing statistical methods that account for temporal and spatial autocorrelation are not practical for remote-sensing datasets that often contain millions of pixels. Here, we first analyze simulated data to show the need to account for both spatial and temporal autocorrelation in time-trend analyses. Second, we present a new statistical approach, PARTS (Partitioned Autoregressive Time Series), to identify underlying patterns and test hypotheses about time trends using all pixels in large remote-sensing datasets. PARTS is flexible and can include, for example, the effects of multiple independent variables, such as land-cover or latitude, on time trends. Third, we use PARTS to analyze global trends in NDVI, focusing on trends in pixels that have not experienced land-cover change. We found that despite the appearance of overall increases in NDVI in all continents, there is little statistical support for these trends except for Asia and Europe, and only in some land-cover classes. Furthermore, we found no overall latitudinal trend in greening for any continent, but some latitude by land-cover class interactions, implying that latitudinal patterns differed among land-cover classes. PARTS makes it possible to identify patterns and test hypotheses that involve the aggregate information from many pixels on a map, thereby increasing the value of existing remote-sensing datasets. • Analyses of global change using satellite data must be statistically valid. • Our new PARTS analysis can analyze map-scale patterns in large datasets. • PARTS flexibly formulates regression models to address global-scale problems. [ABSTRACT FROM AUTHOR]