Advertisement

Topics

Resolving misaligned spatial data with integrated species distribution models.

08:00 EDT 1st April 2019 | BioPortfolio

Summary of "Resolving misaligned spatial data with integrated species distribution models."

Advances in species distribution modeling continue to be driven by a need to predict species responses to environmental change coupled with increasing data availability. Recent work has focused on development of methods that integrate multiple streams of data to model species distributions. Combining sources of information increases spatial coverage and can improve accuracy in estimates of species distributions. However, when fusing multiple streams of data, the temporal and spatial resolutions of data sources may be mismatched. This occurs when data sources have fluctuating geographic coverage, varying spatial scales and resolutions, and differing sources of bias and sparsity. It is well documented in the spatial statistics literature that ignoring the misalignment of different data sources will result in bias in both the point estimates and uncertainty. This will ultimately lead to inaccurate predictions of species distributions. Here, we examine the issue of misaligned data as it relates specifically to integrated species distribution models. We then provide a general solution that builds off work in the statistical literature for the change of support problem. Specifically, we leverage spatial correlation and repeat observations at multiple scales to make statistically valid predictions at the ecologically relevant scale of inference. An added feature of the approach is that addressing differences in spatial resolution between data sets can allow for the evaluation and calibration of lesser quality sources in many instances. Using both simulations and data examples, we highlight the utility of this modeling approach and the consequences of not reconciling misaligned spatial data. We conclude with a brief discussion of the upcoming challenges and obstacles for species distribution modeling via data fusion. This article is protected by copyright. All rights reserved.

Affiliation

Journal Details

This article was published in the following journal.

Name: Ecology
ISSN: 1939-9170
Pages: e02709

Links

DeepDyve research library

PubMed Articles [24108 Associated PubMed Articles listed on BioPortfolio]

Novel predators and anthropogenic disturbance influence spatio-temporal distribution of forest antelope species.

Understanding the effects of anthropogenic disturbance on species' behaviour is crucial for conservation planning, considering the extent of habitat loss. We investigated the influence of anthropogeni...

Mapping knowledge gaps in marine diversity reveals a latitudinal gradient of missing species richness.

A reliable description of any spatial pattern in species richness requires accurate knowledge about species geographical distribution. However, sampling bias may generate artefactual absences within s...

Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010.

Global data sets on the geographic distribution of livestock are essential for diverse applications in agricultural socio-economics, food security, environmental impact assessment and epidemiology. We...

A practical guide for combining data to model species distributions.

Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling...

The impact of urban street tree species on air quality and respiratory illness: A spatial analysis of large-scale, high-resolution urban data.

Urban trees play a key role in reducing greenhouse gas emissions, cleaning air, promoting physical activity, and improving mental health. However, it is still largely unknown how the density and speci...

Clinical Trials [4266 Associated Clinical Trials listed on BioPortfolio]

The Accuracy of Modified TTMB in the Spatial Distribution of Prostate Cancer

Investigators plan to use modified TTMB technology to puncture prostate of participants suspected prostate cancer, especially those with the first negative biopsy , but having a rising PSA...

Resolution of Thrombi in Left Atrial Appendage With Edoxaban

Non-valvular (NV) atrial fibrillation (AF) increases the risk of stroke by approximately fivefold. The atrial thrombi associated with AF are seen within the left atrial appendage (LAA) in ...

Spatial Cognitive Training

This study is designed to see if doing regular training on a spatial imagery task leads to improvements in the ability to do the trained spatial imagery task and in the ability to get arou...

Spatial Epidemiology of Tuberculosis (TB) Infection

Data will be extracted from the NTUH medical records database, geocoded according to the street address so that the case number, incidence, etc could be mapped. The spatial data would be u...

Vestibular Disorder and Visuo-spatial Functions in Deaf Children

The purpose of this study is to determine whether vestibular disorders could affect visuo-spatial cognition. Visuo-spatial cognition will be evaluated using a new computerized test using a...

Medical and Biotech [MESH] Definitions

Methods used to take into account and incorporate spatial autocorrelation and regional variation into regression analysis models of data that has spatial dependency, and also to provide information on the spatial relationships among the variables.

Integration of spatial information perceived by visual and/or auditory CUES.

A process through which individuals encode information about their environmental CUES to facilitate SPATIAL NAVIGATION.

Information application based on a variety of coding methods to minimize the amount of data to be stored, retrieved, or transmitted. Data compression can be applied to various forms of data, such as images and signals. It is used to reduce costs and increase efficiency in the maintenance of large volumes of data.

A concept, developed in 1983 under the aegis of and supported by the National Library of Medicine under the name of Integrated Academic Information Management Systems, to provide professionals in academic health sciences centers and health sciences institutions with convenient access to an integrated and comprehensive network of knowledge. It addresses a wide cross-section of users from administrators and faculty to students and clinicians and has applications to planning, clinical and managerial decision-making, teaching, and research. It provides access to various types of clinical, management, educational, etc., databases, as well as to research and bibliographic databases. In August 1992 the name was changed from Integrated Academic Information Management Systems to Integrated Advanced Information Management Systems to reflect use beyond the academic milieu.

Advertisement
Quick Search
Advertisement
Advertisement

 


DeepDyve research library

Searches Linking to this Article