Using maximum entropy modeling to identify and prioritize red spruce forest habitat in West Virginia

Beane, Nathan R.; Rentch, James S.; Schuler, Thomas M.

doi:10.2737/nrs-rp-23

Cited by 19 publications

(11 citation statements)

References 13 publications

(18 reference statements)

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“…To overcome this problem, the bootstrapping approach adopted in this study splits the data several times and the performance of the model is assessed against the randomly chosen test dataset. The approach has been shown to be effective in situations where data is limited and would warrant the use of all occurrence data with a random partitioning implemented within each MAXENT experimental run (Bean et al 2012;Beane et al 2013;Beane and Rentch 2015).…”

Section: Bootstrappingmentioning

confidence: 99%

Spatial distribution ofVachellia karrooin Zimbabwean savannas (southern Africa) under a changing climate

Shekede

Murwira

Masocha

et al. 2018

Ecological Research

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Climate change projections in southern Africa show a drier and a warmer future climate. It is not yet clear how these changes are going to affect the suitable habitat of bush encroacher woody species in southern African savannas. Maximum Entropy niche modelling technique was used to test the extent to which climate change is likely to affect the suitable habitat of Vachellia karroo in Zimbabwe based on six Global Climate Models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5) and two Representative Concentration Pathways (RCPs) for the 2070s. An overlay analysis was then performed in a Geographic Information System based on the current and future bioclimatically suitable areas for the respective GCMs and RCPs. This was done to determine the potential effect of climate change on the focal species. Results show that temperature related variables are more important in explaining the spatial distribution of V. karroo than precipitation related variables. In addition, results indicate an overall increase in the modelled suitable habitat for V. karroo by the 2070s across the GCMs and RCPs considered in this study. Specifically, the suitable habitat of V. Karroo is projected to increase by a maximum of 57,594 km2 signifying a 69% increase from the current suitable habitat (83,674 km2). The suitable areas are projected to increase in eastern, western and south eastern parts of Zimbabwe. These results imply that improved understanding of the response of woody species to a changing climate is important for managing bush encroachment in savanna ecosystems.

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Section: Bootstrappingmentioning

confidence: 99%

Spatial distribution ofVachellia karrooin Zimbabwean savannas (southern Africa) under a changing climate

Shekede

Murwira

Masocha

et al. 2018

Ecological Research

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“…The maximum entropy principle provides us with a theoretical justification for conducting scientific inference with less informative priors, which is also beneficial in a situation where information is incomplete or dubious [ 43 – 45 ]. To achieve this goal, we could maximize the entropy [ 46 , 47 ] from a somewhat restricted choice of prior distributions.…”

Section: Introductionmentioning

confidence: 99%

Shrinkage in the Bayesian analysis of the GGE model: A case study with simulation

Oliveira

Silva

et al. 2021

PLoS ONE

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The genotype main effects plus the genotype × environment interaction effects model has been widely used to analyze multi-environmental trials data, especially using a graphical biplot considering the first two principal components of the singular value decomposition of the interaction matrix. Many authors have noted the advantages of applying Bayesian inference in these classes of models to replace the frequentist approach. This results in parsimonious models, and eliminates parameters that would be present in a traditional analysis of bilinear components (frequentist form). This work aims to extend shrinkage methods to estimators of those parameters that composes the multiplicative part of the model, using the maximum entropy principle for prior justification. A Bayesian version (non-shrinkage prior, using conjugacy and large variance) was also used for comparison. The simulated data set had 20 genotypes evaluated across seven environments, in a complete randomized block design with three replications. Cross-validation procedures were conducted to assess the predictive ability of the model and information criteria were used for model selection. A better predictive capacity was found for the model with a shrinkage effect, especially for unorthogonal scenarios in which more genotypes were removed at random. In these cases, however, the best fitted models, as measured by information criteria, were the conjugate flat prior. In addition, the flexibility of the Bayesian method was found, in general, to attribute inference to the parameters of the models which related to the biplot representation. Maximum entropy prior was the more parsimonious, and estimates singular values with a greater contribution to the sum of squares of the genotype + genotype × environmental interaction. Hence, this method enabled the best discrimination of parameters responsible for the existing patterns and the best discarding of the noise than the model assuming non-informative priors for multiplicative parameters.

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“…There are several advantages of maximum entropy over other Bayesian modelling approaches including: (1) the need for presence only data for the species of interest; (2) the ability to use continuous and categorical predictor data; (3) the use of deterministic algorithms that converge to a distribution of maximum entropy; (4) maintenance of a stable distribution with limited training data; (5) easily interpretable, continuous output scores; and (6) allowance for assessment of relative importance of predictor variables (Phillips et al , ; Dudik et al , ). Maximum entropy is also less stringent than traditional regression‐based models as variables can possess multicollinearity and be spatially autocorrelated (Hu and Jiang, ; Beane et al , ). Maximum entropy is similar to logistic regression in that each predictor variable is weighted by a constant and the estimated species distribution is divided by a scaling constant that allows all probabilities to sum to one over the extent of interest (Hernandez et al , ).…”

Section: Introductionmentioning

confidence: 99%

Predictive spatial modelling of seasonal bottlenose dolphin (Tursiops truncatus) distributions in the Mississippi Sound

Pitchford

Howard

Shelley

et al. 2015

Aquatic Conservation

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Spatial distribution models (SDMs) have been useful for improving management of species of concern in many areas. This study was designed to model the spatial distribution of bottlenose dolphins among seasons of the year in the Mississippi Sound within the northern Gulf of Mexico. Models were constructed by integrating presence locations of dolphins acquired from line‐transect sampling from 2011–2013 with maps of environmental conditions for the region to generate a likelihood of dolphin occurrence for winter (January–March), spring (April–June), summer (July–September), and autumn (October–December) using maximum entropy. Models were successfully generated using the program MaxEnt and had high predictive capacity for all seasons (AUC (area under curve) > 0.8). Distinct seasonal shifts in spatial distribution were evident including increased predicted occurrence in deepwater habitats during the winter, limited predicted occurrence in the western Mississippi Sound in winter and spring, widespread predicted occurrence over the entire region during summer, and a distinct westward shift of predicted occurrence in autumn. The most important environmental predictors used in SDMs were distance to shore, salinity, and nitrates, but variable importance differed considerably among seasons. Geographic shifts in predicted occurrence probably reflect both direct effects of changing environmental conditions and subsequent changes in prey availability and foraging efficiency. Overall, seasonal models helped to identify preferred habitats for dolphins among seasons of the year and can be used to inform management of this protected species in the northern Gulf of Mexico. Copyright © 2015 John Wiley & Sons, Ltd.

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Using maximum entropy modeling to identify and prioritize red spruce forest habitat in West Virginia

Cited by 19 publications

References 13 publications

Spatial distribution ofVachellia karrooin Zimbabwean savannas (southern Africa) under a changing climate

Spatial distribution ofVachellia karrooin Zimbabwean savannas (southern Africa) under a changing climate

Shrinkage in the Bayesian analysis of the GGE model: A case study with simulation

Predictive spatial modelling of seasonal bottlenose dolphin (Tursiops truncatus) distributions in the Mississippi Sound

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