Marsh et al., 2023 - Google Patents
SDM profiling: A tool for assessing the information-content of sampled and unsampled locations for species distribution modelsMarsh et al., 2023
View HTML- Document ID
- 10110107673154789153
- Author
- Marsh C
- Gavish Y
- Kuemmerlen M
- Stoll S
- Haase P
- Kunin W
- Publication year
- Publication venue
- Ecological Modelling
External Links
Snippet
Species distribution models (SDMs) are key tools in biodiversity and conservation, but assessing their reliability in unsampled locations is difficult, especially where there are sampling biases. We present a spatially-explicit sensitivity analysis for SDMs–SDM profiling …
- 238000005070 sampling 0 title abstract description 103
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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- 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
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Electro-optical investigation, e.g. flow cytometers
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Shen et al. | Time to update the split‐sample approach in hydrological model calibration | |
Tong et al. | A review of assessment methods for cellular automata models of land-use change and urban growth | |
Fieberg et al. | Used‐habitat calibration plots: A new procedure for validating species distribution, resource selection, and step‐selection models | |
Hattab et al. | A unified framework to model the potential and realized distributions of invasive species within the invaded range | |
Field et al. | Optimizing allocation of monitoring effort under economic and observational constraints | |
Tessarolo et al. | Using maps of biogeographical ignorance to reveal the uncertainty in distributional data hidden in species distribution models | |
Landguth et al. | Quantifying the lag time to detect barriers in landscape genetics | |
Alhajeri et al. | High correlation between species‐level environmental data estimates extracted from IUCN expert range maps and from GBIF occurrence data | |
Convertino et al. | Untangling drivers of species distributions: Global sensitivity and uncertainty analyses of MaxEnt | |
Naimi et al. | Where is positional uncertainty a problem for species distribution modelling? | |
Mitchell et al. | Bayesian model selection with BAMM: effects of the model prior on the inferred number of diversification shifts | |
Marsh et al. | SDM profiling: A tool for assessing the information-content of sampled and unsampled locations for species distribution models | |
Ziółkowska et al. | Effects of different matrix representations and connectivity measures on habitat network assessments | |
Zhang et al. | Comparing the prediction of joint species distribution models with respect to characteristics of sampling data | |
Giorgi et al. | Geostatistical methods for disease mapping and visualisation using data from spatio‐temporally referenced prevalence surveys | |
Valle et al. | Extending the Latent Dirichlet Allocation model to presence/absence data: A case study on North American breeding birds and biogeographical shifts expected from climate change | |
Aguejdad et al. | Spatial validation of land use change models using multiple assessment techniques: A case study of transition potential models | |
Foody | Impacts of imperfect reference data on the apparent accuracy of species presence–absence models and their predictions | |
Erickson et al. | Modeling the rarest of the rare: a comparison between multi‐species distribution models, ensembles of small models, and single‐species models at extremely low sample sizes | |
Latombe et al. | zetadiv: an R package for computing compositional change across multiple sites, assemblages or cases | |
Rogerson et al. | Optimal weights for focused tests of clustering using the Local Moran statistic | |
García‐Roselló et al. | A simple method to estimate the probable distribution of species | |
Mir et al. | Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data | |
Tarroso et al. | PHYLIN: an R package for phylogeographic interpolation | |
Tang et al. | ResDisMapper: An r package for fine‐scale mapping of resistance to dispersal |