DIVA Crack Free Download For PC

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DIVA was designed as a handy software for reconstructing ancestral distributions in a phylogeny using dispersal-vicariance analysis.
This is a method in which ancestral distributions are inferred based on a 3D cost matrix derived from a simple biogeographic model,
Unlike other methods in historical biogeography, DIVA does not assume anything about the shape or existence of general biogeographic patterns.

 

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DIVA 2022

DIVA uses a numerical method called Linear Programming to infer ancestral distribution patterns.
DIVA works like the following:
You first give it a phylogenetic tree, where each node represents the possible location of the species at that point in time.
Then, you give it a map of the continents and oceans, and the locations of one or more observed species.
DivA then finds all the best possible locations for the ancestral species, by
combining the geographic constraints of the map with the biogeographic constraints of the tree.
For each possible location, DivA calculates the associated costs, which are simply the distances
between the location of the given node and each of the other nodes in the phylogeny.
It then finds the best solution, which is defined as the one with the lowest total cost.
This solution provides a potential set of geographical constraints on the ancestral locations.
For example, if a particular species is only found on one continent, we can infer that the ancestral
species was not found anywhere else.
DIVA currently supports 1) 3D maps of the continents and oceans, 2)
two-dimensional maps of the continents and a simple H-V to show the presence or absence of each
region, and 3) a simple rectangular map.
There are several different ways that the cost matrix can be constructed:
1) A minimum distance (Manual 3D)
2) Minimum cost in terms of straight line distance
3) A more sophisticated grid-based cost matrix, with more detailed divisions
(Manual 3D).
Here is a description of the basic grid-based cost matrix.
The basic grid-based cost matrix is the default for the Manual 3D option.
It has 5 rows and 7 columns, and it is constructed from the following 4 simple maps:
Geographic Regions:
1) The North America
2) The West Europe
3) The Asia
4) The South America
In addition to the original maps that are shown in the Basic Grid View of DIVA,
DIVA will also be able to show you the same maps in a many other different ways.
For example, DIVA supports 3D views of the continents and oceans, which are shown below:
Country-Vegetation Map:
An annotated country-vegetation map, which identifies the type of vegetation for each country on Earth.
This map is also available as part of the South American Vegetation map.
Country-

DIVA Torrent (Activation Code) [Latest 2022]

DIVA allows a user to enter a phylogenetic tree as well as a set of input parameters for a simple biogeographic model. The biogeographic model is then applied to the tree, which is rendered in a cost matrix. This matrix can be saved and used for other phylogenetic reconstructions.
DIVA is used to reconstruct ancestral distributions on a phylogeny using dispersal-vicariance analysis.
DIVA requires the following user-selected input parameters:
– Node Ages: Date in which an ancestral node was present on the phylogeny
– Tip Ages: Dates in which a particular tip was present on the phylogeny
– Biogeographic Model: A model that dictates how dispersal between nodes was possible.
DIVA allows a user to specify the following cost-parameter values that can be applied to specific node-pairings in the matrix:
– L: number of local vicariance events between nodes.
– P: number of local dispersal events between nodes.
– C: number of clock-like events between nodes.
– W: number of range-wide events between nodes.
DIVA also allows a user to specify the following cost-parameter values that affect all node-pairs in the matrix:
– T: Theta: A value of theta from which the cost matrix is derived.
– X: Eta: A value of eta from which the cost matrix is derived.
– H: Locality Jump: A value of locality jump used to stop at local nodes when interpreting ancestral ranges in a phylogeny
Divergence Time: A value of divergence time used to calculate the time of divergence between node-pairs
DIVA also allows a user to select one or more input files from which the cost matrix is taken.
NOTES
Data:
DIVA currently works with data that is in the same format as BEAST. The cost matrix created by DIVA requires data in a format compatible with BEAST.
Sample code:
The sample code below demonstrates DIVA in operation. Each example is associated with its source file (data, tree, costMatrix)
# Example using nodeAge input file
# Setup CostMatrix from nodeAge data
# Set C, L, P, W to values of 0, 10, 20, and 30
# The number of local vicariance events
# The number of local dispersal events
# The number of clock-like events
# The number
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This page is still under construction. Currently DIVA can reconstruct ancestral ranges for a single species
or for a set of species. Ancestral ranges can be estimated based on a 3D cost matrix. In the latter case, the 3D cost matrix is considered as a prior constraint
and can be converted into a 2D cost matrix to extract a subset of events for subsequent analyses.
Both the 3D and 2D cost matrices can be extended in the future.

DIVA was written in Java and is freely available under the GNU Lesser General Public License.
This is version 1.1.2 (05/25/09).

Prerequisite knowledge:

3D cost matrix:
We assume a phylogenetic tree and a set of nodes,
connected by branches. At each node, a suitable state is chosen and used as a ‘present’ state to calculate a 3D cost matrix.
A cost matrix is a 3D array of real numbers, where each cell represents a cost from the present state to each of its possible descendant states.

2D cost matrix:
The 2D cost matrix is extracted from the 3D matrix by performing a matrix inversion.
This is a consequence of the fact that the 3D matrix can be represented as a sum of two matrices.

Methods:

In the following methods, “?” is replaced by the number of steps needed for a single reconstruction.

classifier_desc:
Returns the classifier that is used to reconstruct ancestral ranges.

classifier_name:
Returns the name of the classifier.

diva_parse_cost_matrix:
Parses a cost matrix from a comma-separated value string.

diva_read_cost_matrix:
Reads a cost matrix from a file.

diva_write_cost_matrix:
Writes a cost matrix to a file.

diva_get_cost_matrix:
Returns a cost matrix as a java array.

diva_get_cost_matrix_2D:
Returns a 2D cost matrix extracted from a 3D cost matrix.

diva_set_cost_matrix:
Sets a cost matrix as the current state.

What’s New in the?

It has been widely recognized that the nature of biogeographic events can only be inferred from comparing the observed geographical distribution of extant species with the ancestral distribution of these species during speciation events. The conventional way of inferring ancestral distributions in a phylogeny was called Dispersal-Vicariance Analysis (DIVA). DIVA assumes that the distributions of species at any node of the tree were identical to their ancestor. This was a difficult assumption to be confirmed or refuted as biogeographic events affect species’ distributions.

One of the many difficulties of the DIVA assumption is that the shape and pattern of general biogeographic trends may not be valid for many taxa.

DIVA v2.0

DIVERSE Bioinformatics has released an updated version of DIVA based on recent studies of Mesoamerican land mammal radiations. DIVA v2.0 includes:

The ability to calculate ancestral distributions for large clades

The ability to assign directions to a cost matrix for large clades

Improved spatial modeling of biogeographic events

DIVA 2.0 has been tested on Aves (birds), Amphibia (amphibians), Mammalia (mammals), Squamata (lizards), Squatiniae (scaled reptiles), and Therocephala (rhino-like ungulates).

DIVA Description:

It has been widely recognized that the nature of biogeographic events can only be inferred from comparing the observed geographical distribution of extant species with the ancestral distribution of these species during speciation events. The conventional way of inferring ancestral distributions in a phylogeny was called Dispersal-Vicariance Analysis (DIVA). DIVA assumes that the distributions of species at any node of the tree were identical to their ancestor. This was a difficult assumption to be confirmed or refuted as biogeographic events affect species’ distributions.

One of the many difficulties of the DIVA assumption is that the shape and pattern of general biogeographic trends may not be valid for many taxa.

DIVA v2.0

DIVERSE Bioinformatics has released an updated version of DIVA based on recent studies of Mesoamerican land mammal radiations. DIVA v2.0 includes:

The ability to calculate ancestral distributions for large clades

The ability to assign directions to a cost matrix for large clades

Improved spatial modeling of biogeographic events

DIVA 2.0 has been tested on Aves (birds), Amphibia (amphibians), Mammalia (mammals), Squamata (lizards), Squatiniae (scaled reptiles), and Therocephala (rhino-like ungulates).

DIVA Description:

It has been widely recognized that the nature of bioge

System Requirements For DIVA:

– At least a Pentium 4 CPU (System Processor) 1.3 GHz or higher.
– 512 MB of RAM (with support for more RAM on up-to-date Linux distributions)
– 64 MB of available video memory on your graphics card (Direct X 9.0c)
– Graphics card is automatically configured, or can be manually adjusted for best performance
Supported
Hardware
Current unsupported hardware:
– NVIDIA GeForce 8 series and 9 series GPUs (see Notes)
– ATI Radeon X1000 and X

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