Ecological Niche Model Layer Derivation for Species Distribution
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Solution Overview
Problem
Current ecological niche modeling (ENM) techniques face challenges in accurately predicting species distribution across varying environmental conditions, especially under climate change scenarios and in identifying suitable habitats for invasive species, due to limitations in data integration and model complexity.
Innovation Solution
A flexible ENM framework that aggregates disparate datasets into a document store with semi-structured attributes, allowing for the generation of niche model layers and the derivation of new data layers, such as food source locations and environmental parameters, to predict future species geospatial locations, using tools like Apache Solr and OpenStreetMap for data indexing and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple disparate datasets are integrated to improve prediction accuracy, then the completeness and resolution of environmental parameters improve, but the system complexity and difficulty of data management increase
Solution Approach 1:
The system segments disparate environmental datasets into standardized niche model layers, each representing a specific environmental parameter (climate, soil, water, land cover). This segmentation allows complex multi-source data to be organized into manageable, standardized units that can be independently processed and combined, resolving the contradiction between data completeness and system complexity.
Solution Approach 2:
The patent creates a universal document store architecture that can accommodate multiple types of environmental datasets with different formats and sources. The standardized niche model layer structure serves as a multi-functional framework that can integrate various data types (climate data, soil data, water data, land cover data) through a common interface, reducing management complexity while maintaining prediction accuracy.
2Reliability
If additional derived environmental parameters are generated to improve model accuracy, then the prediction reliability improves, but the computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing derived environmental parameters (such as topographic features, vegetation indices, and composite environmental indices) in the document store before they are needed for prediction. This allows the prediction model to directly query pre-processed data rather than computing complex derived parameters in real-time, improving reliability while reducing computational requirements during the prediction phase.
3Measurement precision
If high-resolution spatial data is used to improve prediction detail, then the accuracy of species distribution prediction improves, but the data storage requirements and processing complexity increase
Solution Approach 1:
The patent implements local quality by organizing high-resolution spatial data into niche model layers that represent specific environmental parameters at different spatial scales. The system allows users to query and process only the specific high-resolution data layers relevant to their prediction needs, rather than processing all high-resolution data simultaneously. This selective approach maintains prediction detail accuracy while managing data storage and processing complexity.
Data Source
AI summary
An aspect includes aggregating a plurality of disparate datasets into a document store with semi-structured attributes that includes a plurality of documents specifying a plurality of different geo spatial locations and a plurality of different environmental parameters. Niche model layers are generated for the environmental parameters at the geospatial locations based on contents of the document store. The niche model layers include a model layer for each of the different environmental parameters. An additional niche model layer is created for a derived environmental parameter at the geospatial locations based at least in part on one of the previously generated niche model layers. A future geospatial location of a species is predicted based on environmental attributes of the species and contents of at least a subset of the niche model layers. The predicted future geospatial location of the species overlaid on a geographic map is output.