Temporal heterogeneity increases with spatial heterogeneity in ecological communities.

Heterogeneity is increasingly recognized as a foundational characteristic of ecological systems. Under global change, understanding temporal community heterogeneity is necessary for predicting the stability of ecosystem functions and services. Indeed, spatial heterogeneity is commonly used in alternative stable state theory as a predictor of temporal heterogeneity and therefore an early indicator of regime shifts. To evaluate whether spatial heterogeneity in species composition is predictive of temporal heterogeneity in ecological communities, we analyzed 68 community data sets spanning freshwater and terrestrial systems where measures of species abundance were replicated over space and time. Of the 68 data sets, 55 (81%) had a weak to strongly positive relationship between spatial and temporal heterogeneity, while in the remaining communities the relationship was weak to strongly negative (19%). Based on a mixed model analysis, we found a significant but weak overall positive relationship between spatial and temporal heterogeneity across all data sets combined, and within aquatic and terrestrial data sets separately. In addition, lifespan and successional stage were negatively and positively related to temporal heterogeneity, respectively. We conclude that spatial heterogeneity may be a predictor of temporal heterogeneity in ecological communities, and that this relationship may be a general property of many terrestrial and aquatic communities.

Ecology under lake ice.

Winter conditions are rapidly changing in temperate ecosystems, particularly for those that experience periods of snow and ice cover. Relatively little is known of winter ecology in these systems, due to a historical research focus on summer 'growing seasons'. We executed the first global quantitative synthesis on under-ice lake ecology, including 36 abiotic and biotic variables from 42 research groups and 101 lakes, examining seasonal differences and connections as well as how seasonal differences vary with geophysical factors. Plankton were more abundant under ice than expected; mean winter values were 43.2% of summer values for chlorophyll a, 15.8% of summer phytoplankton biovolume and 25.3% of summer zooplankton density. Dissolved nitrogen concentrations were typically higher during winter, and these differences were exaggerated in smaller lakes. Lake size also influenced winter-summer patterns for dissolved organic carbon (DOC), with higher winter DOC in smaller lakes. At coarse levels of taxonomic aggregation, phytoplankton and zooplankton community composition showed few systematic differences between seasons, although literature suggests that seasonal differences are frequently lake-specific, species-specific, or occur at the level of functional group. Within the subset of lakes that had longer time series, winter influenced the subsequent summer for some nutrient variables and zooplankton biomass.

The Environmental Data Initiative: Connecting the past to the future through data reuse.

The Environmental Data Initiative (EDI) is a trustworthy, stable data repository, and data management support organization for the environmental scientist. In a bottom-up community process, EDI was built with the premise that freely and easily available data are necessary to advance the understanding of complex environmental processes and change, to improve transparency of research results, and to democratize ecological research. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, we analyze metadata to provide a general description of EDI's collection of data and its data management philosophy and placement in the repository landscape. We discuss how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, we review measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise.

Defining linkages between the GSC and NSF's LTER program: how the Ecological Metadata Language (EML) relates to GCDML and other outcomes.

The Genomic Standards Consortium (GSC) invited a representative of the Long-Term Ecological Research (LTER) to its fifth workshop to present the Ecological Metadata Language (EML) metadata standard and its relationship to the Minimum Information about a Genome/Metagenome Sequence (MIGS/MIMS) and its implementation, the Genomic Contextual Data Markup Language (GCDML). The LTER is one of the top National Science Foundation (NSF) programs in biology since 1980, representing diverse ecosystems and creating long-term, interdisciplinary research, synthesis of information, and theory. The adoption of EML as the LTER network standard has been key to build network synthesis architectures based on high-quality standardized metadata. EML is the NSF-recognized metadata standard for LTER, and EML is a criteria used to review the LTER program progress. At the workshop, a potential crosswalk between the GCDML and EML was explored. Also, collaboration between the LTER and GSC developers was proposed to join efforts toward a common metadata cataloging designer's tool. The community adoption success of a metadata standard depends, among other factors, on the tools and trainings developed to use the standard. LTER's experience in embracing EML may help GSC to achieve similar success. A possible collaboration between LTER and GSC to provide training opportunities for GCDML and the associated tools is being explored. Finally, LTER is investigating EML enhancements to better accommodate genomics data, possibly integrating the GCDML schema into EML. All these action items have been accepted by the LTER contingent, and further collaboration between the GSC and LTER is expected.

LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes.

Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states.LAGOS-NE contains data for 51 101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states. The database includes 3 data modules for: lake location and physical characteristics for all lakes; ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situ measurements of lake water quality for a subset of the lakes from the past 3 decades for approximately 2600-12 000 lakes depending on the variable. The database contains approximately 150 000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from 87 lake water quality data sets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest and most comprehensive databases of its type because it includes both in situ measurements and ecological context data. Because ecological context can be used to study a variety of other questions about lakes, streams, and wetlands, this database can also be used as the foundation for other studies of freshwaters at broad spatial and ecological scales.

Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse.

Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km(2)). LAGOS includes two modules: LAGOSGEO, with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOSLIMNO, with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration.

A global database of lake surface temperatures collected by in situ and satellite methods from 1985-2009.

Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global data set of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985-2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues.

The relation of H2 S release to SO2 , fumigation of lichens.

Hydrogen sulphide emission in lichens as a response to low concentration SO2 , fumigation was investigated. In an open flow-through system several lichen species were fumigated with 36 ppb SO2 , Two species were also fumigated with higher concentrations (72, 119, 122 and 198 ppb SO2 ,). Hydrogen sulphide emission was monitored concurrently by cryogenic trapping and analysis with gas chromatography. All tested species increased H, S emission significantly in response to fumigation with 36 ppb SO2 . Parmelina tiliacea (L.) Hale and Cladina rangiferina (L.) Wigg. released significantly more H2 S (0.098±0.015 and 0.073±0.013 pmol H2 S g-1 d. wt s-1 , respectively) than Parmelina quercina (Ach.) Hale, Ramalina menziesii Tayl. and Parmelia sulcata Tayl. (0.028 ± 0.01, 0.025±0.014 and 0.023±0.013 pmol H2 S g-1 d. wt s-1 , respectively). Release of H2 S in Hypogymnia physodes was enhanced by increasing SO2 concentrations up to 72 ppb SO2 . No significant difference in H2 S emission in the dark vs. in the light was found. Generally, no correlation was found between photosynthetic activity and H2 S emission for the tested species. Uptake of SO2 was similar for all species, at 24.7 ± 5.6 pmol SO2 g-1 d. wt s-1 in 36 ppb SO2 and increasing at greater SO2 concentrations. Therefore, H2 S-S release represents only 0.11-0.74% of SO2 -S uptake.

Symbiota - A virtual platform for creating voucher-based biodiversity information communities.

We review the Symbiota software platform for creating voucher-based biodiversity information portals and communities. Symbiota was originally conceived to promote small- to medium-sized, regionally and/or taxonomically themed collaborations of natural history collections. Over the past eight years the taxonomically diverse portals have grown into an important resource in North America and beyond for mobilizing, integrating, and using specimen- and observation-based occurrence records and derivative biodiversity information products. Designed to mirror the conceptual structure of traditional floras and faunas, Symbiota is exclusively web-based and employs a novel data model, information linking, and algorithms to provide highly dynamic customization. The themed portals enable meaningful access to biodiversity data for anyone from specialist to high school student. Symbiota emulates functionality of modern Content Management Systems, providing highly sophisticated yet intuitive user interfaces for data entry, batch processes, and editing. Each kind of content provision may be selectively accessed by authenticated information providers. Occupying a fairly specific niche in the biodiversity informatics arena, Symbiota provides extensive data exchange facilities and collaborates with other development projects to incorporate and not duplicate functionality as appropriate.

Bivariate zero-inflated regression for count data: a Bayesian approach with application to plant counts.

Lately, bivariate zero-inflated (BZI) regression models have been used in many instances in the medical sciences to model excess zeros. Examples include the BZI Poisson (BZIP), BZI negative binomial (BZINB) models, etc. Such formulations vary in the basic modeling aspect and use the EM algorithm (Dempster, Laird and Rubin, 1977) for parameter estimation. A different modeling formulation in the Bayesian context is given by Dagne (2004). We extend the modeling to a more general setting for multivariate ZIP models for count data with excess zeros as proposed by Li, Lu, Park, Kim, Brinkley and Peterson (1999), focusing on a particular bivariate regression formulation. For the basic formulation in the case of bivariate data, we assume that Xi are (latent) independent Poisson random variables with parameters λ i, i = 0, 1, 2. A bi-variate count vector (Y1, Y2) response follows a mixture of four distributions; p0 stands for the mixing probability of a point mass distribution at (0, 0); p1, the mixing probability that Y2 = 0, while Y1 = X0 + X1; p2, the mixing probability that Y1 = 0 while Y2 = X0 + X2; and finally (1 - p0 - p1 - p2), the mixing probability that Yi = Xi + X0, i = 1, 2. The choice of the parameters {pi, λ i, i = 0, 1, 2} ensures that the marginal distributions of Yi are zero inflated Poisson (λ 0 + λ i). All the parameters thus introduced are allowed to depend on co-variates through canonical link generalized linear models (McCullagh and Nelder, 1989). This flexibility allows for a range of real-life applications, especially in the medical and biological fields, where the counts are bivariate in nature (with strong association between the processes) and where there are excess of zeros in one or both processes. Our contribution in this paper is to employ a fully Bayesian approach consolidating the work of Dagne (2004) and Li et al. (1999) generalizing the modeling and sampling-based methods described by Ghosh, Mukhopadhyay and Lu (2006) to estimate the parameters and obtain posterior credible intervals both in the case where co-variates are not available as well as in the case where they are. In this context, we provide explicit data augmentation techniques that lend themselves to easier implementation of the Gibbs sampler by giving rise to well-known and closed-form posterior distributions in the bivariate ZIP case. We then use simulations to explore the effectiveness of this estimation using the Bayesian BZIP procedure, comparing the performance to the Bayesian and classical ZIP approaches. Finally, we demonstrate the methodology based on bivariate plant count data with excess zeros that was collected on plots in the Phoenix metropolitan area and compare the results with independent ZIP regression models fitted to both processes.

Preliminary assessment of arbuscular mycorrhizal fungal diversity and community structure in an urban ecosystem.

Arbuscular mycorrhizal fungal (AMF) species richness, composition, spore density and diversity indices were evaluated in the Phoenix metropolitan area, Arizona, USA at 20 sampling sites selected to represent the four predominant land-use types found in the greater urban area: urban-residential, urban non-residential, agriculture and desert. AMF spores were extracted and identified from soil samples and from trap cultures established using soil collected at each site. Data were analyzed according to land use, land-use history, soil chemistry and vegetation characteristics at each site. Current agricultural sites were associated with decreased spore densities and historically agricultural sites with decreased species richness. Overall species composition was similar to that previously reported for the Sonoran desert, but composition at each sampling site was influenced by the vegetation from which samples were collected. Sites with the highest degrees of similarity in AMF species composition were also similar to each other in native plants or land use. Conversely, sites with the lowest similarity in AMF composition were those from which the majority of samples were collected from non-mycorrhizal plants, predominately ectomycorrhizal plants or bare soil. Spores of Glomus microggregatum were most abundant in urban sites, while those of G. eburneum were most abundant in desert and agricultural sites. Further studies are needed to determine the functional implications of shifts in AMF communities in urban ecosystems, including effects on plant primary productivity.

Socioeconomics drive urban plant diversity.

Spatial variation in plant diversity has been attributed to heterogeneity in resource availability for many ecosystems. However, urbanization has resulted in entire landscapes that are now occupied by plant communities wholly created by humans, in which diversity may reflect social, economic, and cultural influences in addition to those recognized by traditional ecological theory. Here we use data from a probability-based survey to explore the variation in plant diversity across a large metropolitan area using spatial statistical analyses that incorporate biotic, abiotic, and human variables. Our prediction for the city was that land use, along with distance from urban center, would replace the dominantly geomorphic controls on spatial variation in plant diversity in the surrounding undeveloped Sonoran desert. However, in addition to elevation and current and former land use, family income and housing age best explained the observed variation in plant diversity across the city. We conclude that a functional relationship, which we term the "luxury effect," may link human resource abundance (wealth) and plant diversity in urban ecosystems. This connection may be influenced by education, institutional control, and culture, and merits further study.