Environmental And Ecological Statistics.pdf
Ailen Eliszewski
This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes. The 21st century statistics community has become increasingly interdisciplinary, bringing a large collection of modern tools to all areas of application in environmental processes. In addition, the environmental community has substantially increased its scope of data collection including observational data, satellite-derived data, and computer model output. The resultant impact in this latter community has been substantial; no longer are simple regression and analysis of variance methods adequate. The contribution of this handbook is to assemble a state-of-the-art view of this interface.
Alan E. Gelfand is the James B. Duke Professor of Statistical Science at Duke University. He is a leader in Bayesian spatial modeling and analysis including a successful book in this area with Banerjee and Carlin.
Richard L. Smith is Mark L. Reed III Distinguished Professor of Statistics and Professor of Biostatistics in the University of North Carolina, Chapel Hill. From 2010-2017 he was also Director of the Statistical and Applied Mathematical Sciences Institute, a Mathematical Sciences Institute supported by the National Science Foundation, and he will continue (through June 2018) as Associate Director of SAMSI. He obtained his PhD from Cornell University and previously held academic positions at Imperial College (London), the University of Surrey (Guildford, England) and Cambridge University. His main research interest is environmental statistics and associated areas of methodological research such as spatial statistics, time series analysis and extreme value theory. He is particularly interested in statistical aspects of climate change research, and in air pollution including its health effects. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, an Elected Member of the International Statistical Institute, and has won the Guy Medal in Silver of the Royal Statistical Society, and the Distinguished Achievement Medal of the Section on Statistics and the Environment, American Statistical Association. In 2004 he was the J. Stuart Hunter Lecturer of The International Environmetrics Society (TIES). He is also a Chartered Statistician of the Royal Statistical Society.
"This is an extremely well-composed book, offering an interdisciplinary exposure to the concepts and methods that are very valuable to perform environmental and ecological data analysis. The contributors are recognized experts in the topics of their writing...Noteworthy features in this book are introducing uncertainty, anisotropy and non-stationarity, threshold exceedance, coenospace, stochasticity, tail-down models, entropy-based design among others...I highly recommend this book to environmental, climate, statistics and computing researchers and practicing professionals."
- Ramalingam Shanmugam, JSCS, Aug 2020
Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R connects applied statistics to the environmental and ecological fields. It follows the general approach to solving a statistical modeling problem, covering model specification, parameter estimation, and model evaluation. The author uses many exa
Measuring changes in ecological conditions through time allows Valley Water, resource agencies, land managers and the public to understand and respond to climate change effects and evolving creek and habitat conditions.
Field surveys for the Guadalupe River watershed reassessment were completed in 2022. Crews collected data at 75 sites in the watershed. These data are available on EcoAtlas. A reassessment report, including the results of the reassessment and a comparison with the 2012 assessment, will be completed and available in late 2024. Assessment reports for other Santa Clara County watersheds can be found under Reports & Documents.
Project D5 continues to help maintain and update the Coyote Creek Native Ecosystem Enhancement Tool (CCNEET), an online decision-support tool to identify and coordinate habitat actions to improve ecological conditions along Coyote Creek, from Anderson Dam to Montague Expressway. Inspired by the need for a watershed approach to environmental resource management, project planning, and permitting, an overarching goal of CCNEET is to help coordinate habitat conservation and enhancement so that multiple projects and limited funding can result in meaningful ecological improvement of the creek. You can learn more about CCNEET here and request access from the contact below.
2015 California Central Coast Healthy Watersheds Project report card with trend analysis. For data and other information, see the Central Coast Regional Water Quality Control Board's Central Coast Ambient Monitoring Program (CCAMP), including the Pajaro River, Uvas-Carnadero and Llagas Creeks.
The program was first passed by voters in 2000 as the Clean, Safe Creeks and Natural Flood Protection Plan, then again in 2012 as the Safe, Clean Water and Natural Flood Protection Program. The renewal of the Safe, Clean Water Program will continue to provide approximately $47 million annually for local projects that deliver safe, clean water, natural flood protection, and environmental stewardship to all the communities we serve in Santa Clara County.
While evaluating ways to improve the 2012 program, Valley Water gathered feedback from more than 21,000 community members. That helped Valley Water create the six priorities for the renewed Safe, Clean Water Program, which are:
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Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.
Soils are considered to be one of the most biodiverse terrestrial habitats1,2,3. Despite this, very little is known about the biodiversity that resides there compared to aboveground biodiversity, especially at the global scale1,4,5. This is surprising given the large number of local-scale biodiversity datasets available in the published literature. A number of studies have amalgamated local scale datasets, primarily for aboveground or marine organisms e.g.6,7, which can then be used for large-scale analyses e.g.8,9. Belowground biodiversity data are often overlooked in these large biodiversity databases4, and thus separate efforts to collate data are just now starting to emerge for certain belowground taxa, particularly microbes e.g.10,11.
Earthworms are involved in a large number of ecosystem functions and services, such as decomposition12, nutrient cycling13 and climate regulation14, amongst others13. In addition, they are often used as bioindicators of soil biodiversity and health15. Earthworms are relatively easy to sample; thus, a large amount of data are available16. Nevertheless, previous attempts to collate earthworm datasets have been geographically restricted17,18 or focused on country or regional species lists (e.g., DriloBASE; ). By collating site-level diversity measures, we can also collect information on factors that might determine community composition, for example, measurements of soil properties or land use and cover.
Here, we describe a global database of local earthworm diversity and associated site-level characteristics from 10,840 sites in 60 countries (Fig. 1)19. Site-level information includes at least one sampled soil property, land use, and habitat cover for just over 58% of sites. Measurements of earthworm species richness (including species lists where available), total abundance, and biomass were collected at the site-level, and for some species occurrences i.e., abundance and biomass of the species recorded at a site. In addition, using expert opinion and details given by data providers, we classified each earthworm species into ecological groups based on their feeding and burrowing behaviours (epigeics, endogeics, anecics, epi-endogeics; more details below20).
Locations of the 276 studies included in the database. Each circle represents the centre of a study (a collection of sites where earthworms were sampled with a consistent method). The size of the circle indicates the number of sites within the study. Transparency is used only for aiding visualisation.
The compilation of this dataset is timely. It can be used to answer long-standing questions in ecology in relation to this important belowground faunal group (e.g., global diversity patterns16). And in light of the IPBES Global Assessment21 and the loss of biodiversity, the dataset has the potential to be used to address the pressing issue of the consequences of environmental change on soil biodiversity. These data are suitable for linking with other soil databases, such as BETSI ( ), a database of soil organism traits22. Linking trait information with site-level diversity would then allow analyses of functional diversity. In addition, as nearly all sites have geographic coordinates, other environmental data layers (e.g., related to climate variables, land use or soil abiotic factors) could be linked to the site-level diversity measures (e.g.16,). Belowground diversity measures could also be linked to similar diversity measurements aboveground, thus enabling investigations across ecosystems to identify patterns of diversity and biodiversity changes23.
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