Nothing Special   »   [go: up one dir, main page]

Wang et al., 2023 - Google Patents

Effects of sample size, data quality, and species response in environmental space on modeling species distributions

Wang et al., 2023

Document ID
3900153952766086727
Author
Wang L
Jackson D
Publication year
Publication venue
Landscape Ecology

External Links

Snippet

Context There have been many studies using species distribution models (SDMs) to predict shifts in species distributions due to environmental changes, but few consider effects of data quantity, data quality, or species response shape. Modeling studies using field-sampled …
Continue reading at link.springer.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/141—Discrete Fourier transforms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G06Q10/0639—Performance analysis
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements

Similar Documents

Publication Publication Date Title
Galante et al. The challenge of modeling niches and distributions for data‐poor species: a comprehensive approach to model complexity
Sillero et al. Common mistakes in ecological niche models
Low et al. Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species
Merow et al. What do we gain from simplicity versus complexity in species distribution models?
Pontius et al. Recommendations for using the relative operating characteristic (ROC)
Hawkins Eight (and a half) deadly sins of spatial analysis
Shabani et al. A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area
Braunisch et al. Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change
Wang et al. Effects of sample size, data quality, and species response in environmental space on modeling species distributions
Zhang et al. Improving prediction of rare species’ distribution from community data
Saupe et al. Tracking a medically important spider: climate change, ecological niche modeling, and the brown recluse (Loxosceles reclusa)
Gábor et al. How do species and data characteristics affect species distribution models and when to use environmental filtering?
Thomassen et al. Spatial modelling and landscape‐level approaches for visualizing intra‐specific variation
Enaruvbe et al. Influence of classification errors on Intensity Analysis of land changes in southern Nigeria
Bowler et al. Improving the community-temperature index as a climate change indicator
Dormann Modelling species’ distributions
Abuzaid et al. Detection of outliers in simple circular regression models using the mean circular error statistic
Barnett et al. Improving estimates of species distribution change by incorporating local trends
Boria et al. A single‐algorithm ensemble approach to estimating suitability and uncertainty: cross‐time projections for four Malagasy tenrecs
Wu et al. Prediction of soil salinity in the Yellow River Delta using geographically weighted regression
Farhate et al. Use of data mining techniques to classify soil CO2 emission induced by crop management in sugarcane field
D’Amen et al. Integrating univariate niche dynamics in species distribution models: A step forward for marine research on biological invasions
Tarroso et al. PHYLIN: an R package for phylogeographic interpolation
Papeş et al. Using maximum entropy to predict the potential distribution of an invasive freshwater snail
Gorospe et al. The importance of sampling design: spatial patterns and clonality in estimating the genetic diversity of coral reefs