Search

Spatially clustered sampling may result in non-independent data that pose challenges for environmental mapping applications. Two outstanding challenges resulting from the use of spatially clustered data for predictive geospatial modelling with machine learning approaches are biased model training and validation. These issues can be severe for popular bagging models such as Random Forest, yet one or both are often ignored or are handled using sub-optimal approaches. We propose to address these challenges using information on both the spatial autocorrelation of map errors and the spatial sampling intensity. This is achieved by applying the residual spatial covariance as a weighting function for the bagging procedure and for the calculation of weighted validation statistics. Using this approach, the full feature space of the sample data is retained during model training and validation. The utility of covariance weighting for these purposes is investigated through extensive simulation with a range of sample clustering configurations. Results are benchmarked against existing approaches. Covariance weighting improved model performance across a range of clustering scenarios but appeared to produce the greatest improvements for highly clustered data. Covariance-weighted validation demonstrated low bias across a broad range of clustering scenarios compared to existing spatial methods. Findings also suggest, though, that conditional Gaussian simulation approaches may perform well when the proportion of clustered data is very high. Covariance weighting is straightforward to implement, computationally efficient, and scales to different sample sizes and spatial extents. • Residual spatial covariance is calculated as weights for clustered data. • Weighted bagging with Random Forest reduced training bias when using clustered data. • Weighted validation produced low bias under a range of clustering scenarios. • Covariance weighting uses both autocorrelation and sample intensity information. • Covariance weighting enables use of the full feature space during validation. [ABSTRACT FROM AUTHOR]

Terrain attributes (e.g. , slope, rugosity) derived in Geographic Information Systems (GIS) from digital terrain models (DTMs) are widely used in both terrestrial and marine ecological studies due to their potential to act as surrogates of species distribution. However, the spatial resolution of DTMs is often altered to match the scale at which species observations were collected. Here, we highlight the significance of adequately reporting the methods used to derive terrain attributes from DTMs and the consequences of their incorrect reporting in ecological studies. To ensure full repeatability of studies, they should report (i) the source and the resolution of the original DTM; (ii) the algorithm used to calculate terrain attributes; (iii) the method used for rescaling (e.g. , aggregating or resampling, using the mean or maximum values); and (iv) the order in which these operations were performed. We contrast the effects of two common scale alteration approaches for the derivation of terrain attributes from DTMs. These two scale alteration methods differ in the step at which the change is performed: (i) the resolution alteration is performed after computing terrain attributes from the original DTM at the native resolution, or (ii) the resolution alteration is performed on the native DTM before computing terrain attributes. While these approaches conceptually do the same thing (i.e., change the resolution of the terrain attributes), we demonstrate that they produce two distinct sets of variables that are not interchangeable and describe different properties of the terrain. In a species distribution modelling (SDM) context, the first approach calculates terrain attribute values within the cell where a species is found, while the second approach calculates terrain attribute values with respect to neighbouring cells. A mutual substitution of the two approaches results in a decrease of models' discrimination ability and in misleading spatial predictions of species probability of occurrence. Regardless of the DTM-derived attribute, we argue that the choice of the approach should be carefully guided by both the ecological scale relevant to the question being asked and the performance of pre-analyses. We emphasize that selected methods be clearly described to encourage reproducibility and proper interpretation of results, thus enabling a better understanding of the role of scale in ecology. • Scale of terrain attributes is often altered to match scale of species occurrences. • Critical information related to scale alteration approach is often undocumented. • Scale alteration approaches differ by the step at which the alteration is performed • Different approaches produce distinct sets of variables and are not interchangeable. • Choice of the approach should be carefully guided by relevant ecological scale. [ABSTRACT FROM AUTHOR]