LARCH-classic: Sustainability of ecological networks

LARCH-classic has been used to compare spatial quality of multiple ecosystems. In these studies, guide species are selected using three criteria: type of habitat, dispersal capacity and area requirements.

The LARCH-classic model is used to answer questions like:

  • Which area is suitable for preserving reed birds ?
  • What spatial configuration of landscape is favourable for a viable population of small mammals ?
  • What size should a nature reserve have for red deer or roe deer ?

The LARCH-classic model is used to determine sustainability of ecological networks for species. It uses different parameters for each species. Habitat is selected from vegetation maps. Suitability for local populations is determined using species-specific area requirements. Ecological networks per species are constructed using dispersal capacity parameters and barrier maps (optionally). The area of the ecological networks is used to evaluate sustainability.

LARCH-SCAN: Spatial Cohesion of Landscape

The LARCH-SCAN model is used to answer questions like:

  • Where are important potential ecological corridors connecting nature areas located ?
  • Which landscape scenario has the best spatial cohesion for a certain species ?
  • What effects do barriers or resistance of the landscape have on spatial cohesion ?

LARCH-SCAN determines suitable habitat for a species. It calculates a relative measure for spatial cohesion. This measure can be used to determine promising areas for species and connections between these areas. For some species the relative measure of connectivity can be transformed into a classification of good, reasonable and bad spatial cohesion.

LARCH-SCAN can take barrier effects and resistance of the landscape into account. This results in an effect on the spatial cohesion. The results of LARCH-SCAN can be used as input to the sustainability evaluation of LARCH-classic. In this way, the effect of barriers and resistance of landscape on the sustainability of ecological networks can be estimated.