A comparison of different landscape metrics between our 300 generated maps (boxplots) and the reference landscape (red dots).

A comparison of different landscape metrics between our 300 generated maps (boxplots) and the reference landscape (red dots).

<p>Blue background: metrices that were used as optimization goals in our genetic algorithm, including <i>number of patches</i> and <i>mean patch area</i> (ha), and white background: additional metrices that were not part of the optimization.</p>

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Bibliographic Details
Main Author: Eyal Goldstein (10024615) (author)
Other Authors: Antonia Deutscher (22529973) (author), Eamon O’Keeffe (22529976) (author), Kerstin Wiegand (491472) (author)
Published: 2025
Subjects:
Genetics
Neuroscience
Ecology
Plant Biology
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
specific projects ).
spatially explicit simulations
open source software
field placement inside
balances structural realism
give spatial representation
stepwise approach ensures
present several examples
recreating spatial patterns
realistic spatial patterns
particular use cases
outlining potential space
div >< p
dynamic landscape generator
agricultural landscape generators
r programming environment
real world maps
generates agricultural landscapes
simple integration within
patterns within
generates landscapes
world maps
spatial </
using real
step approach
patterns 3
package environment
examples include
easy integration
landscape maps
dominated landscapes
simulating maps
landscape contribute
spatial models
workflows using
useful tool
thus oversimplified
therefore making
static objects
remaining adaptable
purpose generator
previous algs
parameter tuning
often used
modelling workflow
increase robustness
high specificity
flexible way
ecological processes
diverse regions
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