A new class of computational methods has been developed to construct distributions of where such monitored organisms are most likely to be found in space and time using this data, and are much more accurate than previous methods when dealing with large sets of data.
Previous methods, called Kernel Methods, were based on associating a parametric distribution, such as a normal distribution, with each location point. The new methods, referred to as LoCoH (local convex hull) methods, are essentially non-parametric kernel methods where the kernel associated with each data point is constructed directly from that point and a given number of its nearest neighbors. These methods have application to all types of ecological and biological resource management problems, but will prove especially useful in evaluating the spatial needs of threatened species and designing parks to conserve them.
This study will be published on February 14, 2007 in PLoS ONE, the international, peer-reviewed, open-access, online publication from the Public Library of Science (PLoS)
Andrew Hyde | Source: alphagalileo
Further information: www.plosone.org
More articles from
Life Sciences:
Plant-eating predator to fight superweed is not magic bullet
14.10.2008 | University of Leicester
Extreme nature helps scientists design nano materials
14.10.2008 | John Innes Centre
Plant-eating predator to fight superweed is not magic bullet
14.10.2008 | Life Sciences
Metallic Silane as a pathway to high-temperature superconductivity in Hydrogen
14.10.2008 | Physics and Astronomy
Worlds largest household longitudinal study launches
14.10.2008 | Studies and Analyses