Using virtual species to study species distributions and model performance

2012 ◽  
Vol 40 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Christine N. Meynard ◽  
David M. Kaplan
Keyword(s):  
Model Performance ◽  
Species Distributions ◽  
Virtual Species ◽  
Study Species

scholarly journals Consensus Forecasting of Species Distributions: The Effects of Niche Model Performance and Niche Properties

PLoS ONE ◽  
2015 ◽  
Vol 10 (3) ◽  
pp. e0120056 ◽  
Author(s):  
Lei Zhang ◽  
Shirong Liu ◽  
Pengsen Sun ◽  
Tongli Wang ◽  
Guangyu Wang ◽  
...  
Keyword(s):  
Model Performance ◽  
Species Distributions ◽  
Niche Model

scholarly journals Analytical guidelines to increase the value of citizen science data: using eBird data to estimate species occurrence

2019 ◽  
Author(s):  
A Johnston ◽  
WM Hochachka ◽  
ME Strimas-Mackey ◽  
V Ruiz Gutierrez ◽  
OJ Robinson ◽  
...  
Keyword(s):  
Data Processing ◽  
Citizen Science ◽  
Model Performance ◽  
Species Distributions ◽  
Ecological Knowledge ◽  
Sample Sizes ◽  
Science Data ◽  
Science Projects ◽  
Wide Range ◽  
The Impact

AbstractCitizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, and variation in effort.To demonstrate addressing key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate two widely applied metrics of species distributions: encounter rate and occupancy probability. For each metric, we assess the impact of data processing steps that either degrade or refine the data used in the analyses. We also test whether differences in model performance are maintained at different sample sizes.Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with: 1) the use of complete checklists (where observers report all the species they detect and identify); and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists and effort variables. Improvements in model performance with data refinement were more evident with larger sample sizes.Here, we describe processes to refine semi-structured citizen science data to estimate species distributions. We demonstrate the value of complete checklists, which can inform the design and adaptation of citizen science projects. We also demonstrate the value of information on effort. The methods we have outlined are also likely to improve other forms of inference, and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data.

Correlative climatic niche models predict real and virtual species distributions equally well

Ecology ◽  
2019 ◽  
Vol 101 (1) ◽  
Author(s):  
Valentin Journé ◽  
Jean‐Yves Barnagaud ◽  
Cyril Bernard ◽  
Pierre‐André Crochet ◽  
Xavier Morin
Keyword(s):  
Species Distributions ◽  
Climatic Niche ◽  
Niche Models ◽  
Virtual Species

scholarly journals virtualspecies, an R package to generate virtual species distributions

Ecography ◽  
2015 ◽  
Vol 39 (6) ◽  
pp. 599-607 ◽  
Author(s):  
Boris Leroy ◽  
Christine N. Meynard ◽  
Céline Bellard ◽  
Franck Courchamp
Keyword(s):  
Species Distributions ◽  
R Package ◽  
Virtual Species

scholarly journals The species of Mentzelia (Loasaceae) in Mexico, part 1: Sectional diversity

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
John Schenk ◽  
Carolina Granados Mendoza ◽  
Andres Eduardo Estrada-Castillón
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
North America ◽  
Species Distributions ◽  
Distribution Patterns ◽  
Species Boundaries ◽  
Western North America ◽  
Species Complexes ◽  
Scanning Electron ◽  
Study Species

Background: Mentzelia (Loasaceae) is a genus of approximately 95 species that are largely distributed in western North America; however, much ambiguity remains regarding species in Mexico. Questions: What species of Mentzelia occur in Mexico and how can they be distinguished? Study species: Mentzelia Methods: Fieldwork, herbarium studies and scanning electron microscopy were carried out to determine the diversity of Mentzelia species in Mexico. Results: Twenty-five species of Mentzelia occur in Mexico, of which four taxa are endemic to the country. Five of the six sections of Mentzelia occur in Mexico. Mentzelia section Mentzelia was the most species rich in Mexico (8 spp.), followed by section Trachyphytum (7 spp.), section Bartonia (6 spp.), section Bicuspidaria (3 spp.), and section Dendromentzelia (1 sp.). The sections have different distribution patterns, with some restricted to few areas and one widespread across most of Mexico. Conclusions: This study is the first treatment of Mentzelia that encompasses all species and regions of Mexico, which includes approximately 26 % of the worldwide Mentzelia species. In-depth studies of the species in the region are needed to abate gaps in our knowledge on the extent of species distributions and to clarify species boundaries among some problematic species complexes.

Predictors of Purchasing a Hearing Aid After an Evaluation Period: A Prospective Study in Dutch Older Hearing Aid Candidates

2019 ◽  
Vol 28 (3S) ◽  
pp. 802-805 ◽  
Author(s):  
Marieke Pronk ◽  
Janine F. J. Meijerink ◽  
Sophia E. Kramer ◽  
Martijn W. Heymans ◽  
Jana Besser
Keyword(s):  
Help Seeking ◽  
Hearing Aid ◽  
Model Performance ◽  
Area Under The Curve ◽  
Secondary Data ◽  
Support Program ◽  
Evaluation Period ◽  
A Prospective Study ◽  
Study Participants ◽  
Baseline Covariates

Purpose The current study aimed to identify factors that distinguish between older (50+ years) hearing aid (HA) candidates who do and do not purchase HAs after having gone through an HA evaluation period (HAEP). Method Secondary data analysis of the SUpport PRogram trial was performed ( n = 267 older, 1st-time HA candidates). All SUpport PRogram participants started an HAEP shortly after study enrollment. Decision to purchase an HA by the end of the HAEP was the outcome of interest of the current study. Participants' baseline covariates (22 in total) were included as candidate predictors. Multivariable logistic regression modeling (backward selection and reclassification tables) was used. Results Of all candidate predictors, only pure-tone average (average of 1, 2, and 4 kHz) hearing loss emerged as a significant predictor (odds ratio = 1.03, 95% confidence interval [1.03, 1.17]). Model performance was weak (Nagelkerke R 2 = .04, area under the curve = 0.61). Conclusions These data suggest that, once HA candidates have decided to enter an HAEP, factors measured early in the help-seeking journey do not predict well who will and will not purchase an HA. Instead, factors that act during the HAEP may hold this predictive value. This should be examined.

Constructing and Deconstructing Concepts

2016 ◽  
Vol 63 (5) ◽  
pp. 249-262 ◽  
Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo
Keyword(s):  
Information Theory ◽  
Model Performance ◽  
Unsupervised Classification ◽  
Family Resemblance ◽  
Category Membership ◽  
Formal Models ◽  
Human Beings ◽  
One Dimensional ◽  
Relational Patterns ◽  
Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

Leaching Kinetics of Atrazine and Inorganic Chemicals in Tilled and Orchard Soils

2014 ◽  
Vol 28 (2) ◽  
pp. 231-237 ◽  
Author(s):  
Lech W. Szajdak ◽  
Jerzy Lipiec ◽  
Anna Siczek ◽  
Artur Nosalewicz ◽  
Urszula Majewska
Keyword(s):  
Reaction Rate ◽  
Inorganic Compounds ◽  
Model Performance ◽  
Order Reaction ◽  
First Order Reaction ◽  
Order Kinetic ◽  
Time Data ◽  
First Order ◽  
Concentration Changes ◽  
Reaction Constants

Abstract The aim of this study was to verify first-order kinetic reaction rate model performance in predicting of leaching of atrazine and inorganic compounds (K+1, Fe+3, Mg+2, Mn+2, NH4 +, NO3 - and PO4 -3) from tilled and orchard silty loam soils. This model provided an excellent fit to the experimental concentration changes of the compounds vs. time data during leaching. Calculated values of the first-order reaction rate constants for the changes of all chemicals were from 3.8 to 19.0 times higher in orchard than in tilled soil. Higher first-order reaction constants for orchard than tilled soil correspond with both higher total porosity and contribution of biological pores in the former. The first order reaction constants for the leaching of chemical compounds enables prediction of the actual compound concentration and the interactions between compound and soil as affected by management system. The study demonstrates the effectiveness of simultaneous chemical and physical analyses as a tool for the understanding of leaching in variously managed soils.

Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction

2019 ◽  
Author(s):  
Michael Withnall ◽  
Edvard Lindelöf ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
Message Passing ◽  
State Of The Art ◽  
A Priori ◽  
Model Performance ◽  
Learning Approaches ◽  
Physical Chemical ◽  
Chemical Descriptor ◽  
Derived Properties ◽  
Memory Schemes ◽  
Hyperparameter Selection

We introduce Attention and Edge Memory schemes to the existing Message Passing Neural Network framework for graph convolution, and benchmark our approaches against eight different physical-chemical and bioactivity datasets from the literature. We remove the need to introduce <i>a priori</i> knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.

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