Sensitivity of Codispersion to Noise and Error in Ecological and Environmental Data

Abstract

Codispersion analysis is a new statistical method developed to assess spatial covariation between two spatial processes that may not be isotropic or stationary. Its application to anisotropic ecological datasets have provided new insights into mechanisms underlying observed patterns of species distributions and the relationship between individual species and underlying environmental gradients. However, the performance of the codispersion coefficient when there is noise or measurement error ("contamination") in the data has been addressed only theoretically. Here, we use Monte Carlo simulations and real datasets to investigate the sensitivity of codispersion to four types of contamination commonly seen in many real-world environmental and ecological studies. Three of these involved examining codispersion of a spatial dataset with a contaminated version of itself. The fourth examined differences in codisperson between plants and soil conditions, where the estimates of soil characteristics were based on complete or thinned datasets. In all cases, we found that estimates of codispersion were robust when contamination, such as data thinning, was relatively low (<15%), but were sensitive to larger percentages of contamination. We also present a useful method for imputing missing spatial data and discuss several aspects of the codispersion coefficient when applied to noisy data to gain more insight about the performance of codispersion in practice.

Species Diversity Associated with Foundation Species in Temperate and Tropical Forests

“…Thus, differences observed at different spatial lags indicate the scale(s) over which spatial processes are operating within the forest plots, whereas differences in the strength or sign of the correlation in different directions are diagnostic of anisotropic spatial processes [20]. To ensure sufficient sample size when computing codispersion coefficients between measurements made in 20-m grid cells (the size of each subplot), we used spatial lags in 20-m intervals from 20 to one-quarter the size of the minimum dimension of each plot [50]. We calculated codispersion coefficients using code custom-written and compiled in C (to reduce computation time), but with a link to R that allows for easy manipulation of input and output datasets [51].…”

Foundation Species Across a Latitudinal Gradient in China

“…Foundation species in forests control species diversity locally within forest stands and at landscape and larger scales by creating habitat for associated flora (e.g., epiphylls, epiphytes, vines, lianas) and modifying soil structure and composition (e.g., Ellison et al, 2005; Brantley et al, 2013; Baiser et al, 2013; Vallejos et al, 2018; Degrassi et al, 2019; Ellison, 2019). Forest foundation species frequently are common, abundant, large trees (e.g., Schweitzer et al, 2004; Ellison et al, 2005; Whitham et al, 2006; Tomback et al, 2016; Ellison et al, 2019), but understory shrubs and treelets also can have foundational characteristics (Kane et al, 2011; Ellison and Degrassi, 2017; Ellison et al, 2019).…”

Current Trends in Forest Ecological Applications of Three-Dimensional Remote Sensing: Transition from Experimental to Operational Solutions?

“…Thus, the Forests special issue "3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function" was conceptualized by the authors of this paper and finally hosted 10 peer-reviewed contributions in which 3D sources of remote sensing data were applied either as a preliminary or auxiliary sources of information to understand, classify, augment, model and predict forest ecological attributes. Geographically, the contributions published within this special issue were well distributed around the globe, including China (four contributions) [32][33][34][35], Canada [36], Germany [37], India [38], Iran [39], Panama [40] and the United States [41]. The geographical distribution of the countries in which the published contributions were carried out are summarized in Figure 1.…”

“…In terms of global climatic regimes and ecological biomes, the temperate biome included the majority of works with seven studies [33][34][35][36][37]39,41], followed by sub-tropical [32,38] and tropical [40] biomes. The topics covered within the published contributions can be divided into multiple groups: There were studies with rather classical applications such as single tree-level prediction of forest structural attributes by terrestrial laser scanning or visual estimation from Google Street View [33,41] and area-based prediction of forest structural attributes by space-borne stereo imagery, laser scanning or combination of passive optical with multi-frequency SAR data [34,35,39].…”

“…A number of works conducted on novel domains were also published, including a correlation between forest spectral burned ratios and height metrics derived from terrestrial laser scanning (using rather conventional height metrics for a novel application) [36], followed by other papers on hitherto rarely-studied topics like association between post-harvest tree root collar geometry and stump height by terrestrial laser scanning [37] and combining space-borne spectral and 3D data for fractional cover mapping of invasive alien woody species [38]. Moreover, Vallejos et al [40] focused on a crucial, yet often neglected, source of statistical problem caused when working with optical remote sensing on quantitative ecological data, co-dispersion errors and data noise, whose results can be directly generalized to any existing source of 3D data. Similarly, habitat fragmentation caused by civil projects in forest ecosystems was surveyed by Li et al [32], who addressed a generally remarkable topic that can be extended to similar cases using or combining 3D data sources like UAV-borne digital surface models.…”