This data represents a wall-to-wall characterization of regional habitat connectivity potential in California for plant and animal species whose movement is inhibited by developed or agricultural land uses. 

The approach uses Omniscape, a modified version of Circuitscape (www.circuitscape.org/) with a moving-window algorithm, to quantify ecological flow (potential connectivity) among all pixels within a 50km radius. Circuitscape treats landscapes as resistive surfaces, where high-quality movement habitat has low resistance and barriers have high resistance. The algorithm incorporates all possible pathways between movement sources and destinations and identifies areas of high flow via low-resistance routes, i.e., routes presenting relatively low movement difficulty because of lower human modification, and thus mortality risk.

This model of present-day connectivity assumes there will be more ‘current flow’, representing wildlife movement, coming from and going to areas that are less modified. Wildlife may encounter barriers and land uses that are not conducive to movement en route. They may avoid moving through these areas entirely or these areas will increase their risk of harm. Land use, energy infrastructure, roads, and night lights are some of the factors that affect the ‘resistance’ to movement in this analysis.

The Omniscape output ‘current flow’ was classified into high, medium and low classes and further categorized by the amount of flow compared to what would be expected in the absence of barriers.  The ‘Channelized’ class has 1.7 times more flow than expected in the absence of barriers and represents the last remaining natural pathway through a modified landscape.  The ‘Intensified’ class has 1.3-1.7 times more flow than expected and represents areas where there are a few remaining natural pathways. The ‘Diffuse’ class has as much flow as expected and represents lands that have many or unlimited movement options. 

Find more information on this dataset at: https://omniscape.codefornature.org/

Download the data. 

This data represents a wall-to-wall characterization of regional habitat connectivity potential in California for plant and animal species whose movement is inhibited by developed or agricultural land uses. 

The approach uses Omniscape, a modified version of Circuitscape (www.circuitscape.org/) with a moving-window algorithm, to quantify ecological flow (potential connectivity) among all pixels within a 50km radius. Circuitscape treats landscapes as resistive surfaces, where high-quality movement habitat has low resistance and barriers have high resistance. The algorithm incorporates all possible pathways between movement sources and destinations and identifies areas of high flow via low-resistance routes, i.e., routes presenting relatively low movement difficulty because of lower human modification, and thus mortality risk.

This model of present-day connectivity assumes there will be more ‘current flow’, representing wildlife movement, coming from and going to areas that are less modified. Wildlife may encounter barriers and land uses that are not conducive to movement en route. They may avoid moving through these areas entirely or these areas will increase their risk of harm. Land use, energy infrastructure, roads, and night lights are some of the factors that affect the ‘resistance’ to movement in this analysis.

The Omniscape output ‘current flow’ was classified into high, medium and low classes and further categorized by the amount of flow compared to what would be expected in the absence of barriers.  The ‘Channelized’ class has 1.7 times more flow than expected in the absence of barriers and represents the last remaining natural pathway through a modified landscape.  The ‘Intensified’ class has 1.3-1.7 times more flow than expected and represents areas where there are a few remaining natural pathways. The ‘Diffuse’ class has as much flow as expected and represents lands that have many or unlimited movement options. 

Find more information on this dataset at: https://omniscape.codefornature.org/

Download the data.