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HyperNiche for Windows 98, 00, ME, NT, XP, Vista, 7, 8, and 10
Multivariate Analysis of Ecological Data
Version 2

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Example Publications Using NPMR and
HyperNiche

Reference DOI,
PDF,
HTML
Response
data
type
Notes
Antoine & McCune (2004) DOI quantitative (growth rates and abundance classes) Local mean NPMR, Gaussian weights, 1-predictor models, small sample size
Berryman & McCune (2006) DOI quantitative (lichen biomass) Local mean NPMR, Gaussian weights used to relate lichen biomass to stand structure and topography. Based on the response surfaces observed with NPMR, they chose final models of three types: NPMR, nonlinear regression, and multiple linear regression.
Binder & Ellis (2008) DOI binary (species presence and randomly generated pseudoabsences) Local mean NPMR, Gaussian weights. Modeled responses to pollutant loads and climate variables under various climate change scenarios; randomization tests; evaluated spatial autocorrelation
Casazza et al (2007) DOI quantitative (number of endemic taxa) Local mean NPMR, uniform weights (“SpOcc” model). Modeled diversity of endemic plants in relation to glacial limit, substrate type, and thermoclimatic belts.
Cristofolini et al. (2008) DOI quantitative (lichen diversity) Local mean NPMR, uniform weights (“SpOcc” model). Modeled overall lichen diversity and nitrophytic lichen diversity in response to pollutant concentrations, stand characteristics, and other environmental variables.
DeBano et al. (2010) PDF quantitative (insects trapped per month) Local mean NPMR, Gaussian weights, modeling insect pests trapped per month against weather and other environmental variables. Numerous 3D wireframe response surfaces, sensitivity analysis, and randomization tests. Clear 1-page explanation of NPMR.
Derr et al. (2007) DOI quantitative (species richness in relation to geography and community ordination scores) Local linear NPMR, Gaussian weights. Compared fit of species richness to four different sets of predictors: topographic+geographic, vascular plants, the combination of the two preceding sets, and community ordination scores.
Ellis & Coppins (2007) DOI quantitative (species richness) Local mean NPMR, Gaussian weights. Stepwise selection of predictors representing climate and forest structure; randomization test. Predictors were selected from a pool of 15 variables and evaluated with a randomization test. Models were used to generate predictions based on future climate scenarios.
Ellis et al. (2007a) DOI binary (species presence) Local mean NPMR, Gaussian weights. Modeled species presence against climatic predictors. Applied models to climate change scenarios.
Ellis et al. (2007b) DOI binary (species presence) for 26 species Local mean NPMR, Gaussian weights. Modeled species presence against climatic predictors; included randomization tests and AUCs. They present NPMR models for many species, depicted them geographically rather than response surfaces in the predictor space.
Engelbrecht et al. 2007 DOI NPMR for an ecological purpose Example of NPMR with respect to a single predictor. The probability of species occurrence was modelled versus dry season duration.
Fenton & Bergeron (2008) DOI Species richness and evenness Assessed the relative roles of age and habitat in creating and maintaining species diversity. "The use of multiple overlapping data sets [predictors] with NPMR and subsequent comparison permits complex interactions between different variables to be teased out."
Flitcroft (2008) DOI quantitative (log density of a species) Log density of juvenile salmon regressed against habitat characteristics , using local mean and Gaussian weights. Used NPMR because of failure of parametric modeling.
Giordani (2007) DOI quantitative (diversity) Local mean NPMR, uniform weights (“SpOcc” model). Diversity regressed against pollutants and other environmental variables.
Giordani & Incerti (2008) DOI quantitative (species abundance) Local mean NPMR, uniform weights ("SpOcc" model). Regressed many species against macroclimatic variables. Combined results with multivariate community analysis of the same data set.
Grundel & Pavlovic (2008) DOI quantitative (bird species density) Local mean NPMR, Gaussian weights, modeling the density of many bird species in relationship to numerous habitat factors. This paper gives a lucid explanation of NPMR, three dimensional response surfaces, and some nice examples of interacting nonlinear responses.
Hosten et al. (2007) PDF Grazing utilization Local mean NPMR, Gaussian weights. Modeled the relationship of maximum utilization and average utilization to environmental factors, vegetative descriptors, and management activities
Jovan (2003) DOI quantitative (species abundance classes) Local mean NPMR, Gaussian weights, 1- to 3-predictor models
Jovan & McCune (2005) PDF Species abundance in relation to scores on NMS ordinations Local mean NPMR, Gaussian weights. Used optimum value for a species on one axis while fitting the response curve to another axis. In effect this slices a response surface along a particular plane.
Jovan & McCune (2006) DOI Nitrophile abundance in relation to elevation. Local mean NPR, Gaussian weights, 1 predictor. Compared to nonlinear regression and simple linear regression.
Kohler (2007) DOI quantitative, log(abundance of species) Local mean NPR, Gaussian weights. Regressed population size (density of an insect species) against "hemlock woolly adelgid population score"
Lintz et al. (2011) DOI binary and quantitative (simulated and real data) Local mean NPMR with Gaussian weights. Compared the performance of Random Forests, Classification and Regression Trees, and NPMR using a large variety of 3D response surfaces. They found: "The accuracy of each method depends on the threshold strength and diagonality of the original data structure with each method differing in degree of dependence (Fig. 4). The accuracy of most methods decreases as diagonality increases and threshold strength decreases with the exception of NPMR with continuous data... NPMR demonstrates the least variability (seen as quantile bars in Fig. 4) and the greatest accuracy (seen as medians in Fig. 4) compared to the other methods for a given response shape. The sensitivities of modeling methods to shape attributes of data structure arises from features specific to each modeling method, which manifest in visual differences of predicted surfaces for different shapes (Fig. 5). For our subsequent analyses using real ecological data, we choose the most accurate and robust method we test, NPMR."
McCune (2006) DOI binary (species presence) and quantitative (species abundance) Local mean NPMR, Gaussian weights, medium and large sample sizes, simulated and real data, comparison of linear, logistic and NPMR models.
McCune (2007) PDF quantitative (potential direct incident radiation) Local linear NPMR, Gaussian weights, with slope, aspect, and latitude as predictors
McCune et al. (2003) PDF binary (species presence) Local mean NPMR, uniform weighting function (predates inclusion of Gaussian weights in HyperNiche), 1- and 2-predictor models
Miller et al. (2007) DOI quantitative (community ordination scores, species richness and density of particular functional groups) Local mean NPMR, Gaussian weights, used to model stream insect communities in relation to longitudinal gradients. Detailed, clear explanation of NPMR, including model specification and sensitivity analysis. Nice exposition of detecting interactions.
Minuto et al. (2006) DOI quantitative (genetic diversity) Local mean NPMR, uniform weights; regressed genetic diversity against geography (latitude, longitude, elevation) and population features (number of individuals, occupancy area, occupancy rate).
Ponzetti et al. (2007) DOI quantitative (species abundance classes and community ordination scores) Local linear NPMR, Gaussian weights; regressed many species against ordination scores; also quantitative community ordination scores regressed against disturbance and cheatgrass.
Ponadera & Potapova (2007) DOI quantitative (abundance of diatom species) Local linear NPMR, Gaussian weights. Regional-scale analysis of diatom species abundance in relation to water chemistry.
Rood et al. (2010) DOI quantitative (willow cover) Local mean NPMR with Gaussian weights, regressing willow cover against environmental variables. Illustrations include 2D response curves superimposed on scatterplots.
Potapova & Wintel (2006) PDF quantitative (% relative abundance and log-transformed cell densities Local linear and local mean NPMR, Gaussian weights. Modeled abundance of three diatom species in relation to water quality variables. Includes a table comparing fits for local linear and local mean models. In general, the fits were slightly higher for local linear models.
Reusser & Lee (2008) DOI binary (species presence) in benthic estuarine and coastal communities Local mean NPMR, Gaussian weights, used to model species presence in relation to habitat and geographic variables at two scales. "NPMR generally performs well at both spatial scales and that distributions of non-indigenous species are predicted as well as those of native species."
Wedderburn et al. (2007) DOI quantitative (fish species abundance) Used NPR to relate individual fish species abundance to salinity.
Yost (2006) HTML binary (species presence) Local mean NPMR with Gaussian weights, regressing presence of selected species against site factors, past management, and stand characteristics.
Yost (2008) DOI binary (species presence) Local mean NPMR with Gaussian weights. "NPMR was compared with logistic regression (LR) by building reduced models from variables selected as best by NPMR and full models from variables identified as significant with a forward stepwise process and further manual testing. LogB was used to select models with the highest predictive capability. NPMR models were less complex and had higher predictive capability than LR for all modeling approaches. Spatial coordinates were among the most powerful predictors and the modeling approach with physiographic and stand structural variables together was the most improved relative to the average frequency of occurrence. GIS probability maps produced with the application of the physiographic models showed good spatial congruence between high probability values and plots that contained CLUN. NPMR proved to be a reliable probability modeling and mapping tool that could be used as the analytical link between monitoring and quantifying the status and trends of vegetation resources."