Long-term Ecological Research Data Research Articles

INTEGRATING LONG TERM ECOLOGICAL RESEARCH (LTER) AND MARINE PROTECTED AREA MANAGEMENT: CHALLENGES AND SOLUTIONS

Long Term Ecological Research (LTER) generates unique data sets that have the potential to identify and quantify trends in ecological processes that may be difficult to detect at lower temporal resolutions. Even though many LTER sites are within protected areas, they do not necessarily produce data that are well aligned with the objectives of the institutions and individuals who manage and govern these sites. There are several potential reasons for this, including: i) insufficient information provided by the LTER on socio-ecological variables of relevance to management; ii) a mismatch between the spatial scale of the data collected by the LTER and the scale of data needed to inform management decisions; iii) a lack of inclusion of policy-makers in crucial steps of LTER project implementation, including experimental design and analysis, and; iv) an absence or insufficiency of formal or informal mechanisms for incorporating LTER data into environmental decision-making and protected area governance. Using examples from recently implemented LTER in the Costa dos Corais Environmental Protected Area (known as the PELD-CCAL), we reflect on how some of these challenges can be addressed and provide general recommendations for increasing the conservation management and policy relevance of LTER projects in Brazil.

Popler: An r package for extraction and synthesis of population time series from the long‐term ecological research (LTER) network

Abstract Population dynamics play a central role in the historical and current development of fundamental and applied ecological science. The nascent culture of open data promises to increase the value of population dynamics studies to the field of ecology. However, synthesis of population data is constrained by the difficulty in identifying relevant datasets, by the heterogeneity of available data and by access to raw (as opposed to aggregated or derived) observations. To obviate these issues, we built a relational database, popler, and its R client, the library "popler". popler accommodates the vast majority of population data under a common structure, and without the need for aggregating raw observations. The "popler" R library is designed for users unfamiliar with the structure of the database and with the SQL language. This R library allows users to identify, download, explore and cite datasets salient to their needs. We implemented popler as a PostgreSQL instance, where we stored population data originated by the United States Long Term Ecological Research (LTER) Network. Our focus on the US LTER data aims to leverage the potential of this vast open data resource. The database currently contains 305 datasets from 25 LTER sites. popler is designed to accommodate automatic updates of existing datasets, and to accommodate additional datasets from LTER as well as non‐LTER studies. The combination of the online database and the R library "popler" is a resource for data synthesis efforts in population ecology. The common structure of popler simplifies comparative analyses, and the availability of raw data confers flexibility in data analysis. The "popler" R library maximizes these opportunities by providing a user‐friendly interface to the online database.

Making LTER Data FAIR: A workbench using DEIMS datasets and GBIF Tools

DEIMS-SDR (Dynamic Ecological Information Management System - Site and dataset registry, Wohner et al. 2019) is one of the largest repositories of long-term ecological research (LTER) datasets. It provides sophisticated searching tools by metadata elements and identifiers for all the 930 contained datasets, most of them from European sites. Whereas datasets' metadata are highly structured and searchable, datasets themselves have little standardization in terms of content, identifiers or license, making data integration difficult or cumbersome. Adopting the data FAIR guiding principles(Wilkinson et al. 2016) for LTER data would result in better data integration and reutilization to support knowledge discovery and innovation in ecological research. The Global Biodiversity Information Facility (GBIF 2019a). is the largest repository of species distribution data in the world, providing access to more than a billion records from over 43,000 datasets. GBIF is a good example of FAIR principles implementation: GBIF data is highly standardized, using Darwin Core (Wieczorek et al. 2012) for data and ecological metadata language (EML, Fegraus et al. 2005) for metadata, allowing record-level search; and has implemented globally unique and persistent identifiers for datasets and downloads. Relevant in this context is that GBIF has recently introduced a new data format intended for monitoring projects and sampling event protocols (GBIF 2019b). In this presentation, we explore the suitability of GBIF data formats and workflows to serve LTER datasets, and the work it may take to transform typical LTER datasets into these formats. For this exercise, we use some datasets available via the DEIMS platform, corresponding to the same territory, (Sierra Nevada, Spain (e.g. Bonet 2016, Bonet 2018) and transform them into the GBIF's sample-based Event core publish them in the GBIF data network, and then perform an analysis to assess how the standardized datasets work in practice, both among themselves and also with typical “occurrence-based” GBIF datasets. Finally, we discuss our findings and make recommendations for the GBIF and LTER communities.

Comparison of large-scale citizen science data and long-term study data for phenology modeling.

Large‐scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local‐scale studies for their analyses. This choice has potential trade‐offs related to spatial scale, observer variance, and interannual variability. Here we explored this issue with phenology by comparing models built using data from the large‐scale, citizen science USA National Phenology Network (USA‐NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites. We built statistical and process based phenology models for species common to each data set. From these models, we compared parameter estimates, estimates of phenological events, and out‐of‐sample errors between models derived from both USA‐NPN and LTER data. We found that model parameter estimates for the same species were most similar between the two data sets when using simple models, but parameter estimates varied widely as model complexity increased. Despite this, estimates for the date of phenological events and out‐of‐sample errors were similar, regardless of the model chosen. Predictions for USA‐NPN data had the lowest error when using models built from the USA‐NPN data, while LTER predictions were best made using LTER‐derived models, confirming that models perform best when applied at the same scale they were built. This difference in the cross‐scale model comparison is likely due to variation in phenological requirements within species. Models using the USA‐NPN data set can integrate parameters over a large spatial scale while those using an LTER data set can only estimate parameters for a single location. Accordingly, the choice of data set depends on the research question. Inferences about species‐specific phenological requirements are best made with LTER data, and if USA‐NPN or similar data are all that is available, then analyses should be limited to simple models. Large‐scale predictive modeling is best done with the larger‐scale USA‐NPN data, which has high spatial representation and a large regional species pool. LTER data sets, on the other hand, have high site fidelity and thus characterize inter‐annual variability extremely well. Future research aimed at forecasting phenology events for particular species over larger scales should develop models that integrate the strengths of both data sets.

Influence of soil temperature and moisture on the dissolved carbon, nitrogen, and phosphorus in organic matter entering lake ecosystems

Concentrations of terrestrially derived dissolved organic matter (DOM) have been increasing in many north temperate and boreal lakes for over two decades. The concentration of DOM in lakes is influenced by a number of environmental factors, but there is still considerable debate about how the availability of terrestrial DOM, and associated dissolved nitrogen and phosphorus, may be affected by drivers of climatic change. Using experimental and observational methods, we considered how changes in soil temperature and moisture affected the composition of carbon, nitrogen, and phosphorus entering freshwater lakes. In our experiment, organic soil cores were collected from the wetland shoreline of a darkly-stained seepage lake in northern Wisconsin, USA and manipulated in laboratory with temperature and moisture treatments. During the 28-day study, soil leachate was sampled and analyzed for optical properties of DOM via UV/Vis absorbance, as well as concentrations of dissolved organic carbon (DOC), total dissolved nitrogen, and total dissolved phosphorus (TDP). DOM optical properties were particularly sensitive to moisture, with drier scenarios resulting in DOM of lower molecular weight and aromaticity. Warmer temperatures led to lower DOC and TDP concentrations. To consider long-term relationships between climate and lake chemical properties, we analyzed long-term water chemistry data from two additional Wisconsin lakes from the long term ecological research (LTER) project in a cross correlation analysis with Palmer drought severity index data. Analysis of the LTER data supported our experimental results that soil moisture has a significant effect on the quality of DOM entering lakes and that climate may significantly affect lake chemical properties. Although unexpected in terms of DOM loading for climate change scenarios, these results are consistent with patterns of decomposition in organic soils and may be attributed to an increase in soil DOM processing.

Ensuring the quality of data packages in the LTER network data management system

Considerable data analyses use automated workflows to ingest data from public repositories, and rely on data packages of high structural quality. The Long Term Ecological Research (LTER) Network now screens all packages entering its long-term archive to ensure completeness and quality, and to ascertain that metadata and data are structurally congruent, i.e., that the data typing and formats expressed in metadata agree with that found in data entities. The EML Congruence Checker (ECC) system is a component of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA), and operates on data tables in packages described with Ecological Metadata Language using the EML Data Manager Library, written in Java. Checking is extensible for other data types and customizable via a template. Reports are retained as part of the submitted data package, and summaries here reflect the general usability of LTER data for a variety of purposes. On average in 2015, site-contributed data in the LTER catalog were 95% compliant (valid) with the current suite of checks.

Megapolitan Political Ecology and Urban Metabolism in Southern Appalachia

Drawing on megapolitan geographies, urban political ecology, and urban metabolism as theoretical frameworks, this article theoretically and empirically explores megapolitan political ecology. First, we elucidate a theoretical framework in the context of southern Appalachia and, in particular, the Piedmont megapolitan region, suggesting that the megapolitan region is a useful scale through which to understand urban metabolic connections that constitute this rapidly urbanizing area. We also push the environmental history and geography literature of the U.S. South and southern Appalachia to consider the central role urban metabolic connections play in the region's pressing social and environmental crises. Second, we empirically illuminate these human and nonhuman urban metabolisms across the Piedmont megapolitan region using data from the Coweeta Long-Term Ecological Research (LTER) program, especially highlighting a growing “ring of asphalt” that epitomizes several developing changes to patterns of metabolism. The conclusion suggests that changing urban metabolisms indicated by Coweeta LTER data, ranging from flows of people to flows of water, pose a complicated problem for regional governance and vitality in the future.