An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

/pdf/experimental-design-and-data-analysis-for-biologists-8ybva8d53x.pdf

Experimental Design and Data Analysis for Biologists

https://www.clw.csiro.au/aclep/documents/McBratney%20et%20al.%202003.pdf

On digital soil mapping

Growing interest in spatial ecology is promoting new approaches to the study of seed dispersal, one of the key processes determining the spatial structure of plant populations. Seed-dispersion patterns vary among plant species, populations and individuals, at different distances from parents, different microsites and different times. Recent field studies have made progress in elucidating the mechanisms behind these patterns and the implications of these patterns for recruitment success. Together with the development and refinement of mathematical models, this promises a deeper, more mechanistic understanding of dispersal processes and their consequences.

/pdf/spatial-patterns-of-seed-dispersal-their-determinants-and-3jyzqf0p7a.pdf

Spatial patterns of seed dispersal, their determinants and consequences for recruitment.

Humans now strongly influence almost every major aquatic ecosystem, and their activities have dramatically altered the fluxes of growth-limiting nutrients from the landscape to receiving waters. Unfortunately, these nutrient inputs have had profound negative effects upon the quality of surface waters worldwide. This review examines how eutrophication influences the biomass and species composition of algae in both freshwater and costal marine systems. An overview of recent advances in algae-related eutrophication research is presented. In freshwater systems, a summary is presented for lakes and reservoirs; streams and rivers; and wetlands. A brief summary is also presented for estuarine and coastal marine ecosystems. Eutrophication causes predictable increases in the biomass of algae in lakes and reservoirs; streams and rivers; wetlands; and coastal marine ecosystems. As in lakes, the response of suspended algae in large rivers to changes in nutrient loading may be hysteretic in some cases. The inhibitory effects of high concentrations of inorganic suspended solids on algal growth, which can be very evident in many reservoirs receiving high inputs of suspended soils, also potentially may occur in turbid rivers. Consistent and predictable eutrophication-caused increases in cyanobacterial dominance of phytoplankton have been reported worldwide for natural lakes, and similar trends are reported here both for phytoplankton in turbid reservoirs, and for suspended algae in a large river. A remarkable unity is evident in the global response of algal biomass to nitrogen and phosphorus availability in lakes and reservoirs; wetlands; streams and rivers; and coastal marine waters. The species composition of algal communities inhabiting the water column appears to respond similarly to nutrient loading, whether in lakes, reservoirs, or rivers. As is true of freshwater ecosystems, the recent literature suggests that coastal marine ecosystems will respond positively to nutrient loading control efforts. Our understanding of freshwater eutrophication and its effects on algal-related water quality is strong and is advancing rapidly. However, our understanding of the effects of eutrophication on estuarine and coastal marine ecosystems is much more limited, and this gap represents an important future research need. Although coastal systems can be hydrologically complex, the biomass of marine phytoplankton nonetheless appears to respond sensitively and predictably to changes in the external supplies of nitrogen and phosphorus. These responses suggest that efforts to manage nutrient inputs to the seas will result in significant improvements in coastal zone water quality. Additional new efforts should be made to develop models that quantitatively link ecosystem-level responses to nutrient loading in both freshwater and marine systems.

http://globalpnetwork.net/sites/default/files/Smith_2003_ESPR_eutrophication_review.pdf

Eutrophication of freshwater and coastal marine ecosystems a global problem

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1015&context=agronomyfacpub

Soil health and sustainability: managing the biotic component of soil quality

https://ccc.inaoep.mx/~mdprl/documentos/17062015.pdf

Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps

Geostatistics brings to ecology novel tools for the interpretation of spatial patterns of organisms, of the numerous environmental components with which they in- teract, and of the joint spatial dependence between organisms and their environment. The purpose of this paper is to use data from the ecological literature as well as from original research to provide a comprehensive and easily understood analysis ofgeostatistics' manner of modeling and methods. The traditional geostatistical tool, the variogram, a tool that is beginning to be used in ecology, is shown to provide an incomplete and misleading summary of spatial pattern when local means and variances change. Use of the non-ergodic covariance and correlogram provides a more effective description of lag-to-lag spatial dependence because the changing local means and variances are accounted for. Indicator transforma- tions capture the spatial patterns of nominal ecological variables like gene frequencies and the presence/absence of an organism and of subgroups of a population like large or small individuals. Robust variogram measures are shown to be useful in data sets that contain many data outliers. Appropriate removal of outliers reveals latent spatial dependence and patterns. Cross-variograms, cross-covariances, and cross-correlograms define the joint spa- tial dependence between co-occurring organisms. The results of all of these analyses bring new insights into the spatial relations of organisms in their environment.

/pdf/geostatistical-tools-for-modeling-and-interpreting-2g0wrgusgj.pdf

Geostatistical tools for modeling and interpreting ecological spatial dependence

Subsurface tile drainage from row-crop agricultural production systems has been identified as a major source of nitrate entering surface waters in the Mississippi River basin. Noncontrollable factors such as precipitation and mineralization of soil organic matter have a tremendous effect on drainage losses, nitrate concentrations, and nitrate loadings in subsurface drainage water. Cropping system and nutrient management inputs are controllable factors that have a varying influence on nitrate losses. Row crops leak substantially greater amounts of nitrate compared with perennial crops; however, satisfactory economic return with many perennials is an obstacle at present. Improving N management by applying the correct rate of N at the optimum time and giving proper credits to previous legume crops and animal manure applications will also lead to reduced nitrate losses. Nitrate losses have been shown to be minimally affected by tillage systems compared with N management practices. Scientists and policymakers must understand these factors as they develop educational materials and environmental guidelines for reducing nitrate losses to surface waters.

/pdf/nitrate-nitrogen-in-surface-waters-as-influenced-by-climatic-2z15wej9kr.pdf

Nitrate nitrogen in surface waters as influenced by climatic conditions and agricultural practices.

Soil spatial variability

We study two new informative path planning problems that are motivated by the use of aerial and ground robots in precision agriculture. The first problem, termed sampling traveling salesperson problem with neighborhoods ( Sampling TSPN), is motivated by scenarios in which unmanned ground vehicles (UGVs) are used to obtain time-consuming soil measurements. The input in SamplingTSPN is a set of possibly overlapping disks. The objective is to choose a sampling location in each disk and a tour to visit the set of sampling locations so as to minimize the sum of the travel and measurement times. The second problem concerns obtaining the maximum number of aerial measurements using an unmanned aerial vehicle (UAV) with limited energy. We study the scenario in which the two types of robots form a symbiotic system—the UAV lands on the UGV, and the UGV transports the UAV between deployment locations. This paper makes the following contributions. First, we present an $\operatornamewithlimits{\mathcal {O}}(\frac{r_{\max }}{r_{\min }})$ approximation algorithm for SamplingTSPN , where $r_{\min }$ and $r_{\max }$ are the minimum and maximum radii of input disks. Second, we show how to model the UAV planning problem using a metric graph and formulate an orienteering instance to which a known approximation algorithm can be applied. Third, we apply the two algorithms to the problem of obtaining ground and aerial measurements in order to accurately estimate a nitrogen map of a plot. Along with theoretical results, we present results from simulations conducted using real soil data and preliminary field experiments with the UAV.

David J. Mulla

Papers

Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps

Geostatistical tools for modeling and interpreting ecological spatial dependence

Nitrate nitrogen in surface waters as influenced by climatic conditions and agricultural practices.

Soil spatial variability

Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture