Sensitivity of codispersion to noise and error in ecological and environmental data

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.Comment: 20 pages, 14 figure

3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed

Space Programs Summary 37-66. Volume 1 - Flight projects, 1 September - 31 October 1970

Engineering and scientific information for Mars 71, Mariner Venus-Mercury, and Viking Mars program

Using codispersion analysis to characterize spatial patterns in species co-occurrences

Visualizing and quantifying spatial patterns of co-occurrence (i.e., of two or more species, or of species and underlying environmental variables) can suggest hypotheses about processes that structure species assemblages and their relevant spatial scales. Statistical models of spatial co-occurrence generally assume that underlying spatial processes are isotropic and stationary but many ecologically realistic spatial processes are anisotropic and non-stationary. Here, we introduce codispersion analysis to ecologists and use it to detect and quantify anisotropic and nonstationary patterns and their relevant spatial scales in bivariate co-occurrence data. Simulated data illustrated that codispersion analysis can accurately characterize complex spatial patterns. Analysis of co-occurrence of common tree species growing in a 35-ha plot revealed both positive and negative codispersion between different species; positive codispersion values reflected positive correlation in species abundance (aggregation), whereas negative codispersion values reflected negative correlation in species abundance (segregation). Comparisons of observed patterns with those simulated using two different null models showed that the codispersion of most species pairs differed significantly from random expectation. We conclude that codispersion analysis can be a useful exploratory tool to guide ecologists interested in modeling spatial processes.Organismic and Evolutionary Biolog

Spatial Assessment of Boreal Forest Carbon

University of Minnesota Ph.D. dissertation. June 2015. Major: Natural Resources Science and Management. Advisor: Paul Bolstad. 1 computer file (PDF); xi, 204 pages.The ability to accurately map and monitor forest carbon (C) has gained global attention as countries seek to comply with international agreements to mitigate climate change. However, attaining precise estimates of forest C storage is challenging due to the inherent heterogeneity occurring across different scales. To develop cost-effective sampling protocols, there is a need for more unbiased estimates of the current C stock, its distribution among forest compartments and its variability across different scales. As a contribution to this work, this dissertation used high-resolution field measurements of C collected from different forest compartments across a boreal forest stand in South East Norway. In the first paper, we combined the use of airborne scanning light detection and ranging (lidar) systems with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools across the forest. We found that predictor variables from lidar derived metrics delivered precise models of above and belowground tree C, which comprised the largest of the measured C pool in our study. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. In the search for an effective tool to measure and monitor forest C pools, we found the capabilities of lidar to map forest C encouraging. In the second paper, we used a geostatistical approach to analyze the fine-scale heterogeneity of the soil organic layer (forest floor) C storage. Our results showed that the C stocks were highly variable within each plot, with spatial autocorrelation distances < 3 m. Further, we established that a minimum of 20 to 25 inventory samples is needed to determine the organic layer C stock with a precision of �0.5 kg C m-2 in inventory plots of ~2000 m2. In the third paper, we investigated how the short-range spatial variability of organic layer C affects sampling strategies aiming to monitor and detect changes in the C stock. We found that sample repeatability rapidly declines with sample separation distance, and the a priori sample sizes needed to detect a change a fixed change in the organic layer C stock vary by a factor of ~4 over 15 to 125 cm separation distance. Unless care is taken by the surveyor to ensure spatial sampling precision, substantially larger samples sizes, or longer time intervals between baseline sampling and revisit are required to detect a change. In the final paper, we utilized the nested sampling protocol to investigate the spatial variability of organic layer C across different scales and incorporated inventory expenses in the development of a cost-optimal sampling approach. Because precise estimates are costly to obtain, it is of great interest for surveyors to develop cost-efficient sampling protocols aimed at maximizing the spatial coverage, while minimizing the estimate variance. We found that the majority of the estimate variance is confined within small subplots (100 m2) of the forest (25 km2), emphasizing the importance of considering the short-range variability when conducting a large-scale inventory. Further, this chapter demonstrated how optimal allocation of sampling units (plot, subplot and sample) is not only a function of the variance component within that dimension, but also changes with the sampling unit costs and the acceptable margin of error. We found that the costs of conducting an organic layer C inventory could be reduced by more than 60% by increasing the inventory uncertainty from �0.25 Mg C ha-1 to �0.5 Mg C ha-1. Finally, we established that sampling costs can be reduced with as much 80% by conducting a double sampling procedure that utilizes the correlation between organic layer C stock (r = 0.79 to 0.85) and measurements of layer thickness

Incorporating plant community structure in species distribution modelling: a species co-occurrence based composite approach

Species distribution models (SDM) with remotely sensed (RS) imagery is widely used in ecological studies and conservation planning, and the performance is frequently limited by factors including small plant size, small numbers of observations, and scattered distribution patterns. The focus of my thesis was to develop and evaluate alternative SDM methodologies to deal with such challenges. I used a record of nine endemic species occurrences from the Athabasca Sand Dunes in northern Saskatchewan to assess five different modelling algorithms including modern regression and machine learning techniques to understand how species distribution characteristics influence model prediction accuracies. All modelling algorithms showed robust performance (>0.5 AUC), with the best performance in most cases from generalized linear models (GLM). The threshold selection for presence-absence analysis highlights that actively selecting the optimum level is the best approach compared to the standard high threshold approach as with the latter there is a potential to deliver inconsistent predictions compared to observed patterns of occurrence frequency. The development of the composite-SDM framework used small-scale plant occurrence and UAV imagery from Kernen Prairie, a remnant Fescue prairie in Saskatoon, Saskatchewan. The evaluation of the effectiveness of five algorithms clearly showed that each method was capable of handling a wide range of low to high-frequency species with strong GLM performance irrespective of the species distribution pattern. It is critical to highlight that, although GLM is computationally efficient, the method does not compromise accuracy for simplicity. The inclusion of plant community structure using image clustering methods found similar accuracy patterns indicating limited advantages of using high-resolution images. The study found for high-frequency species that prediction accuracy declines to be as low as the accuracy expected for low-frequency species. Higher prediction confidence was often observed with low-frequency species when the species occurred in a distinct habitat that was visually and spectrally distinct from the surroundings. Such a pattern is in contrast to species widespread in different grassland habitats where distinct spectral signatures were lacking. The study has substantial evidence to state that the optimal algorithmic performance is tied to a balanced number of presences and absences in the data. The co-occurrence analysis also revealed significant co-occurrence patterns are most common at moderate levels of species occurrence frequencies. The research does not indicate any consistent accuracy changes between baseline direct reflectance models and composite-SDM framework. Although accuracy changes were marginal with the composite-SDM framework, the method is well capable of influencing associated type 1 and type 2 error rates of the classification