Importance Of Spatial Autocorrelation Research Articles

Importance of Spatial Autocorrelation in Machine Learning Modeling of Polymetallic Nodules, Model Uncertainty and Transferability at Local Scale

Machine learning spatial modeling is used for mapping the distribution of deep-sea polymetallic nodules (PMN). However, the presence and influence of spatial autocorrelation (SAC) have not been extensively studied. SAC can provide information regarding the variable selection before modeling, and it results in erroneous validation performance when ignored. ML models are also problematic when applied in areas far away from the initial training locations, especially if the (new) area to be predicted covers another feature space. Here, we study the spatial distribution of PMN in a geomorphologically heterogeneous area of the Peru Basin, where SAC of PMN exists. The local Moran’s I analysis showed that there are areas with a significantly higher or lower number of PMN, associated with different backscatter values, aspect orientation, and seafloor geomorphological characteristics. A quantile regression forests (QRF) model is used using three cross-validation (CV) techniques (random-, spatial-, and cluster-blocking). We used the recently proposed “Area of Applicability” method to quantify the geographical areas where feature space extrapolation occurs. The results show that QRF predicts well in morphologically similar areas, with spatial block cross-validation being the least unbiased method. Conversely, random-CV overestimates the prediction performance. Under new conditions, the model transferability is reduced even on local scales, highlighting the need for spatial model-based dissimilarity analysis and transferability assessment in new areas.

Predicting the Influence of Multi-Scale Spatial Autocorrelation on Soil-Landform Modeling

Although numerous soil–landform modeling investigations have documented the effects and importance of spatial autocorrelation (SAC), little is known about how to predict the magnitude of those effects from the degree of SAC in the model variables. In this study, we quantified the SAC inherent in soil and landform variables of four widely divergent pedogeomorphological systems around the world to examine general relationships between SAC and spatial regression model results. Spatial regressions were performed by incorporating spatial filters, extracted by spatial eigenvector mapping, into non-spatial models as additional predictor variables. Results indicated that incorporation of spatial filters improved the performance of the non-spatial regressions—increases in R² and decreases in both Akaike Information Criterion (AIC) and residual SAC were observed. More remarkable was that the degree of improvement was strongly and linearly related (i.e., proportional) to the level of SAC inherently possessed by each soil variable. Our findings show that spatial modeling outcomes are sensitive to the degree of SAC possessed by a soil property when treated as a response variable. Thus, the level of SAC present in a soil variable can serve as a direct indicator for how much improvement a non-spatial model will undergo if that SAC is appropriately taken into account.

Factors influencing large-scale distribution of two sister species of pine voles (Microtus lusitanicus and Microtus duodecimcostatus): the importance of spatial autocorrelation

The Lusitanian pine vole ( Microtus lusitanicus (Gerbe, 1879)) and the Mediterranean pine vole ( Microtus duodecimcostatus de Selys-Longchamps, 1839) are sister species with burrowing habits and a restricted European distribution. Our aim was to assess the relative effect of environmental, soil, and spatial characteristics on the distribution of these species in Portugal, and obtain predicted occurrence maps for each species, particularly to identify areas of sympatry. We used spatial eigenvector mapping (SEVM) to describe the spatial autocorrelation in species data, and we partitioned the variance in species distributions to quantify the relative effects of environmental, soil, and spatial characteristics. The spatial variables explained the major part of variability in both species distributions and were more important than environmental or soil variables. The Lusitanian pine vole occurs in areas outside landscape units of grassland, higher rainfall, frost, and cambisols, with mostly acid soils, lower abundance of litosols, and presence of solonshaks. The Mediterranean pine vole is distributed in grassland areas within intermediate values of soil pH, dominated by litosols and luvisols, and lower rainfall, frost, and cambisols. Our results showed disjunct sympatric areas of small size and a parapatry boundary for the centre of Portugal, suggesting that contact zones are probably narrow.

“Space—The Final Frontier”

Regional planning agencies often project future income at the neighborhood level to determine future needs for transportation, jobs, social services, and amenities. Despite a decade of methodological advances regarding the importance of spatial autocorrelation in altering or reducing the reliability of regression estimates, few forecasters have tried to include such spatial relationships in their neighborhood-level projections. Part of the reason is that controlling for spatial autocorrelation is complex and can require expensive software. The authors use a free and user-friendly software package to estimate spatial effects in forecasting and then show how to develop and utilize proxy variables that can mitigate such autocorrelation. They illustrate how the failure to include such controls in Southern California could lead to overestimates of income in poor areas and hence a tendency to underserve needy areas.

Can niche‐based distribution models outperform spatial interpolation?

ABSTRACTAim Distribution modelling relates sparse data on species occurrence or abundance to environmental information to predict the population of a species at any point in space. Recently, the importance of spatial autocorrelation in distributions has been recognized. Spatial autocorrelation can be categorized as exogenous (stemming from autocorrelation in the underlying variables) or endogenous (stemming from activities of the organism itself, such as dispersal). Typically, one asks whether spatial models explain additional variability (endogenous) in comparison to a fully specified habitat model. We turned this question around and asked: can habitat models explain additional variation when spatial structure is accounted for in a fully specified spatially explicit model? The aim was to find out to what degree habitat models may be inadvertently capturing spatial structure rather than true explanatory mechanisms.Location We used data from 190 species of the North American Breeding Bird Survey covering the conterminous United States and southern Canada.Methods We built 13 different models on 190 bird species using regression trees. Our habitat‐based models used climate and landcover variables as independent variables. We also used random variables and simulated ranges to validate our results. The two spatially explicit models included only geographical coordinates or a contagion term as independent variables. As another angle on the question of mechanism vs. spatial structure we pitted a model using related bird species as predictors against a model using randomly selected bird species.Results The spatially explicit models outperformed the traditional habitat models and the random predictor species outperformed the related predictor species. In addition, environmental variables produced a substantial R2 in predicting artificial ranges.Main conclusions We conclude that many explanatory variables with suitable spatial structure can work well in species distribution models. The predictive power of environmental variables is not necessarily mechanistic, and spatial interpolation can outperform environmental explanatory variables.

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

Spatial autocorrelation in species’ distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non‐spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially‐explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species’ distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse‐grained models with a large extent.

The importance of spatial autocorrelation, extent and resolution in predicting forest bird occurrence

Concerns about declines in forest biodiversity underscore the need for accurate estimates of the distribution and abundance of organisms at large scales and at resolutions that are fine enough to be appropriate for management. This paper addresses three major objectives: (i) to determine whether the resolution of typical air photo-derived forest inventory is sufficient for the accurate prediction of site occupancy by forest birds. We compared prediction success of habitat models using air photo variables to models with variables derived from finer resolution, ground-sampled vegetation plots. (ii) To test whether incorporating spatial autocorrelation into habitat models via autologistic regression increases prediction success. (iii) To determine whether landscape structure is an important factor in predicting bird distribution in forest-dominated landscapes. Models were tested locally (Greater Fundy Ecosystem [GFE]) using cross-validation, and regionally using an independent data set from an area located ca. 250 km to the northwest (Riley Brook [RB]). We found significant positive spatial autocorrelation in the residuals of at least one habitat model for 76% (16/21) of species examined. In these cases, the logistic regression assumption of spatially independent errors was violated. Logistic models that ignored spatial autocorrelation tended to overestimate habitat effects. Though overall prediction success was higher for autologistic models than logistic models in the GFE, the difference was only significantly improved for one species. Further, the inclusion of spatial covariates did little to improve model performance in the geographically discrete study area. For 62% (13/21) of species examined, landscape variables were significant predictors of forest bird occurrence even after statistically controlling for stand-level variability. However, broad spatial extents explained less variation than local factors. In the GFE, 76% (16/21) of air photo and 81% (17/21) of ground plot models were accurate enough to be of practical utility (AUC > 0.7). When applied to RB, both model types performed effectively for 55% (11/20) of the species examined. We did not detect an overall difference in prediction success between air photo and ground plot models in either study area. We conclude that air photo data are as effective as fine resolution vegetation data for predicting site occupancy for the majority of species in this study. These models will be of use to forest managers who are interested in mapping species distributions under various timber harvest scenarios, and to protected areas planners attempting to optimize reserve function.