Relationship between Occupational and Physical Therapist Students’ Belongingness and Perceived Competence in the Clinic using the Ascent to Competence Scale

Clinical education experiences (CEEs) serve an essential role in physical therapist (PT) and occupational therapist (OT) student development. The Ascent to Competence Scale (ACS) measures valuable attributes of belongingness, competence, and welcoming associated with CEE placement. The purpose of this study was to examine the relationship between PT and OT students’ belongingness and perceived competence during CEE using the ACS. A survey consisting of 35 questions from the ACS measuring students’ feelings of belongingness and perceived competence in the clinic was administered to PT and OT students from 7 Midwest universities. Respondents rated statements using a 5-point Likert-type scale (“never true” to “always true”). Ascent to Competence items were aggregated to develop belongingness and perceived competence constructs. One hundred nineteen (67.2% PT, 32.8% OT) of 509 (23.4% response) eligible students completed the survey. Results of a linear regression analysis showed belongingness in the clinical environment significantly predicted perceived competence measures, F(1, 117) = 182.389; P = r2 = .609, y(comp) = .721(Xbel) + 1.249. Cumulative weeks in CEE and practice environment did not contribute to the predictive model. The analysis lends further support to the role that belongingness plays in advancing perceived competence during the CEE. The results suggest that supportive clinical education environments can positively impact student learning by promoting a sense of belongingness among student therapists

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

Long-Term Citizen-Collected Data Reveal Geographical Patterns and Temporal Trends in Lake Water Clarity

We compiled a lake-water clarity database using publically available, citizen volunteer observations made between 1938 and 2012 across eight states in the Upper Midwest, USA. Our objectives were to determine (1) whether temporal trends in lake-water clarity existed across this large geographic area and (2) whether trends were related to the lake-specific characteristics of latitude, lake size, or time period the lake was monitored. Our database consisted of >140,000 individual Secchi observations from 3,251 lakes that we summarized per lake-year, resulting in 21,020 summer averages. Using Bayesian hierarchical modeling, we found approximately a 1% per year increase in water clarity (quantified as Secchi depth) for the entire population of lakes. On an individual lake basis, 7% of lakes showed increased water clarity and 4% showed decreased clarity. Trend direction and strength were related to latitude and median sample date. Lakes in the southern part of our study-region had lower average annual summer water clarity, more negative long-term trends, and greater interannual variability in water clarity compared to northern lakes. Increasing trends were strongest for lakes with median sample dates earlier in the period of record (1938–2012). Our ability to identify specific mechanisms for these trends is currently hampered by the lack of a large, multi-thematic database of variables that drive water clarity (e.g., climate, land use/cover). Our results demonstrate, however, that citizen science can provide the critical monitoring data needed to address environmental questions at large spatial and long temporal scales. Collaborations among citizens, research scientists, and government agencies may be important for developing the data sources and analytical tools necessary to move toward an understanding of the factors influencing macro-scale patterns such as those shown here for lake water clarity

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

Data from: Multi-scaled drivers of ecosystem state: quantifying the importance of the regional spatial scale

The regional spatial scale is a vital linkage for the informed extrapolation of results from local to continental scales to address broad-scale environmental problems. Among-region variation in ecosystem state is commonly accounted for by using a regionalization framework, such as an ecoregion classification. Rarely have alternative regionalization frameworks been tested for variables measuring ecosystem state, nor have the underlying relationships with the variables that are used to define them been assessed. In this study, we asked two questions: (1) How much among-region variation is there for ecosystems and does it differ by regionalization framework? (2) What are the likely causes of this among-region variation? We present a case study using a large data set of lake water chemistry, uni- and multi-scaled hydrogeomorphic and anthropogenic driver variables that likely influence lake chemistry at the subcontinental scale, and seven existing regionalization frameworks. We used multilevel models to quantify and explain within- and among-region variation in lake water chemistry. Our models account for local driver variables of ecosystem variation within regions, differences in regional mean ecosystem state (i.e., random intercepts in multilevel models), and differences in relationships between local drivers and ecosystem state by region (i.e., random slopes in multilevel models). Using one of the best performing regionalization frameworks (Ecological Drainage Units), we found that for lake phosphorus and alkalinity: (1) a majority of all the variation in water chemistry among the studied lakes occurred among regions, (2) very few regional-scale landscape driver variables were required to explain among-region variation in lake water chemistry, (3) a much higher proportion of the total variation among lakes was explained at the regional scale than at the local scale, and (4) some relationships between local-scale driver variables and lake water chemistry varied by region. Our results demonstrate the importance of considering the regional spatial scale for broad-scale research and ecosystem management and conservation. Our quantitative approach can be easily applied to other response variables, ecosystem types, geographic areas, and spatial extents to inform ecosystem responses to global environmental stressors

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

Cheruvelil EPA-NLAPP 6-state lake-landscape database

Lake data were compiled for six U.S. states: Maine (N=~593), New Hampshire (N=~651), Ohio (N=~55), Iowa (N=~117), Michigan (N=~557), and Wisconsin (N=~347). Lake water chemistry were sampled from the epilimnion. Data were collected from databases maintained by state agencies responsible for monitoring lakes under the Federal Clean Water Act, which requires standard procedures and quality assurance and quality control protocols. Lakes with surface area ≥ 1 ha and maximum depth ≥ 2 m were included in the dataset. Each lake was assigned a unique identifier. We collected data on LAKES (chemistry, clarity and depth); NATURAL LANDSCAPE FEATURES (catchment area, lake elevation, and features obtained from GIS (hydrology, runoff, precip, baseflow); and HUMAN IMPACT FEATURES (Land use/cover (NLCD) in 500m buffer around lakes, roads in 500m buffer, human census data within the smallest unit available (county subdivision). We also quantified land cover, land use, groundwater hydrology, and several other geographic variables within the Ecological Drainage Unit (EDU) region. Lake data came from the following state agency sources: Maine Department of Environmental Protection, Maine Department of Inland Fisheries and Wildlife’s Lake Survey table (1/21/03); New Hampshire Department of Environmental Services; Michigan Department of Environmental Quality; Wisconsin Department of Natural Resources; Ohio Environmental Protection Agency; and Iowa State University Limnology Laboratory (Joint Iowa DNR / ISU Project). The purpose of the dataset was to investigate lake and landscape controls on lake water chemistry across broad geographic regions

Long-term citizen-collected data reveal geographical patterns and temporal trends in lake water clarity.

We compiled a lake-water clarity database using publically available, citizen volunteer observations made between 1938 and 2012 across eight states in the Upper Midwest, USA. Our objectives were to determine (1) whether temporal trends in lake-water clarity existed across this large geographic area and (2) whether trends were related to the lake-specific characteristics of latitude, lake size, or time period the lake was monitored. Our database consisted of >140,000 individual Secchi observations from 3,251 lakes that we summarized per lake-year, resulting in 21,020 summer averages. Using Bayesian hierarchical modeling, we found approximately a 1% per year increase in water clarity (quantified as Secchi depth) for the entire population of lakes. On an individual lake basis, 7% of lakes showed increased water clarity and 4% showed decreased clarity. Trend direction and strength were related to latitude and median sample date. Lakes in the southern part of our study-region had lower average annual summer water clarity, more negative long-term trends, and greater inter-annual variability in water clarity compared to northern lakes. Increasing trends were strongest for lakes with median sample dates earlier in the period of record (1938-2012). Our ability to identify specific mechanisms for these trends is currently hampered by the lack of a large, multi-thematic database of variables that drive water clarity (e.g., climate, land use/cover). Our results demonstrate, however, that citizen science can provide the critical monitoring data needed to address environmental questions at large spatial and long temporal scales. Collaborations among citizens, research scientists, and government agencies may be important for developing the data sources and analytical tools necessary to move toward an understanding of the factors influencing macro-scale patterns such as those shown here for lake water clarity

A description of lake hydrologic classes and the spatial extents for land use measurements.

<p>A diagram explaining the features of the 3 different lakes classes and the spatial extents tested in this study. The lake zones are shown on the isolated lake diagram, the stream zone is shown in the DR_ST diagram, and the network catchment is shown in the DR_ST-LK diagram. However, such scales are also quantified for some (but not all) of the different lake classes as depicted in the bottom half of the diagram. na is not applicable.</p