Teleporters, tunnels & time : Understanding warp devices in videogames

Catchment land uses, particularly agriculture and urban uses, have long been recognized as major drivers of nutrient concentrations in surface waters. However, few simple models have been developed that relate the amount of catchment land use to downstream freshwater nutrients. Nor are existing models applicable to large numbers of freshwaters across broad spatial extents such as regions or continents. This research aims to increase model performance by exploring three factors that affect the relationship between land use and downstream nutrients in freshwater: the spatial extent for measuring land use, hydrologic connectivity, and the regional differences in both the amount of nutrients and effects of land use on them. We quantified the effects of these three factors that relate land use to lake total phosphorus (TP) and total nitrogen (TN) in 346 north temperate lakes in 7 regions in Michigan, USA. We used a linear mixed modeling framework to examine the importance of spatial extent, lake hydrologic class, and region on models with individual lake nutrients as the response variable, and individual land use types as the predictor variables. Our modeling approach was chosen to avoid problems of multi-collinearity among predictor variables and a lack of independence of lakes within regions, both of which are common problems in broad-scale analyses of freshwaters. We found that all three factors influence land use-lake nutrient relationships. The strongest evidence was for the effect of lake hydrologic connectivity, followed by region, and finally, the spatial extent of land use measurements. Incorporating these three factors into relatively simple models of land use effects on lake nutrients should help to improve predictions and understanding of land use-lake nutrient interactions at broad 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 km² ). LAGOS includes two modules: LAGOS[subscript]GEO , 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 LAGOS[subscript]LIMNO , 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.Keywords: LAGOS, Integrated database, Data harmonization, Database Ecoinformatics, Macrosystems ecology, Landscape limnology, Water qualityKeywords: LAGOS, Integrated database, Ecoinformatics, Data harmonization, Water quality, Data sharing, Landscape limnology, Macrosystems ecology, Database documentation, Data reus

Surface water connectivity affects lake and stream fish species richness and composition

Stream and lake fishes are important economic and recreational resources that respond to alterations in their surrounding watersheds and serve as indicators of ecological stressors on aquatic ecosystems. Research suggests that fish species diversity is largely influenced by surface water connectivity, or the lack thereof; however, few studies consider freshwater connections and their effect on both lake and stream fish communities across broad spatial extents. We used fish data from 559 lakes and 854 streams from the midwestern/northeastern United States to examine the role of surface water connectivity on fish species richness and community composition. We found that although lakes and streams share many species, connectivity had a positive effect on species richness across lakes and streams and helped explain species composition. Taking an integrated approach that includes both lake and stream fish communities and connectivity among freshwaters helps inform scientific understanding of what drives variation in fish species diversity at broad spatial scales and can help managers who are faced with planning for state, regional, or national scale monitoring and restoration.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Soranno_MI_LULC

We compiled lake water quality and land use/land cover (LULC) data on Michigan lakes. We broadly define lakes to include both lakes and reservoirs. MSU’s Remote Sensing and GIS Outreach and Services (RS/GIS) staff conducted all landscape analyses that have been incorporated into this database. At RS/GIS, Justin Booth and Sarah Acmoody were the analysts creating the landscape portions of the database. Lakes were selected that had historical water quality data collected from ~ 1975-1985 by the Michigan Department of Environmental Quality. The lakes were further selected based on whether they had lake depth associated with them, lake classifications, and other metrics. All lakes that the MI-DEQ sampled only were > 20 ha and had public access

Synthesis of results by spatial extent and nutrient.

<p>The dark grey shading corresponds to evidence for a stronger relationship at one or more spatial extents than at the other non-shaded spatial extents based on both R² and the absolute value of the slope (larger positive or negative number indicating a stronger effect). Dashes indicate a lack of an ecologically relevant relationship (in which either the slope CIs overlap zero or an R² value close to zero). LULC is the land use/land cover type, Wetl. is wetland, Nut. is nutrient, Loc. is local catchment, 100m-S is the 100 m zone around inflowing streams, Net. is the network catchment. The DR_ST lake class includes drainage lakes with streams flowing in; and the DR_ST-LK lake class includes drainage lakes with streams flowing in and upstream lakes ≥ 10 ha.</p

Region-specific slopes in models of land use-lake total phosphorus (TP).

<p>For the relationships between TP and individual LULC types by lake class, spatial extent, and region, the region-specific slopes (colored circles, without the CI’s, for clarity) from mixed-effects models with varying intercepts and varying slopes of the LULC type. The light blue vertical bars indicate whether the varying slope parameter in this model was significant using a likelihood ratio test. We also show the fixed effect slopes including the non-significant estimates from Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135454#pone.0135454.g004" target="_blank">4</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135454#pone.0135454.g005" target="_blank">5</a> as black diamonds for comparison only. The region codes are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135454#pone.0135454.t001" target="_blank">Table 1</a>.</p

Results from the unconditional mixed-effect models.

<p>The models are for total phosphorus (TP) and total nitrogen (TN) with varying intercepts and no covariates, testing for regional differences in average lake nutrient concentration. Lake classes as for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135454#pone.0135454.t001" target="_blank">Table 1</a>.</p><p>^a ICC = intraclass correlation coefficient, the percent of the total variation among lakes that is among-region variation.</p><p>Results from the unconditional mixed-effect models.</p

Numbers of study lakes by hydrologic lake class within each region.

<p>DR_ST are stream-connected drainage lakes, DR_ST-LK are stream-lake connected drainage lakes. The location of the regions are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135454#pone.0135454.g001" target="_blank">Fig 1</a> by code.</p><p>^a The regions used in this study are Ecological Drainage Units [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135454#pone.0135454.ref032" target="_blank">32</a>]. The three-digit codes are ordered roughly from southeast to northwest.</p><p>Numbers of study lakes by hydrologic lake class within each region.</p

Region-specific slopes in models of land use-lake total nitrogen (TN).

<p>For the relationships between TN and individual LULC types by lake class, spatial extent, and region, the region-specific slopes (colored circles, without the CI’s, for clarity) from mixed-effects models with varying intercepts and varying slopes of the LULC type. The light blue vertical bars indicate whether the varying slope parameter in this model was significant using a likelihood ratio test. We also show the fixed effect slopes including the non-significant estimates from Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135454#pone.0135454.g004" target="_blank">4</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135454#pone.0135454.g005" target="_blank">5</a> as black diamonds for comparison only. The region codes are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135454#pone.0135454.t001" target="_blank">Table 1</a>.</p

Synthesis of results for the relationship between lake nutrients and LULC.

<p>Categories and descriptions as for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135454#pone.0135454.g006" target="_blank">Fig 6</a>. The dark grey shading corresponds to either spatial extent (the fixed effects), region (the random effects), or lake class (comparison across models) being important for a specific land use/cover (LULC)-nutrient combination based on visual inspection (and some statistical evaluation) of graphs and model output. The numbers at the bottom row indicate the number of the tested LULC types (4 total) in each column that were shown to be important predictors of lake nutrient concentrations. The dashes indicate no relationship.</p