<｜begin▁of▁sentence｜>### Instruction:
As a wildlife biologist deeply engaged in studying the behavior and ecology of various species, imagine you're investigating a network of interconnected habitats in a forest ecosystem. You have data representing eight distinct habitat patches, each potentially linked to others by various ecological connections. In this scenario, you're interested in visualizing this network to gain insights into habitat connectivity.

Given that each pair of habitat patches may have a connection probability of 0.5, akin to the likelihood of wildlife movement or ecological interactions between them, you're tasked with using a layout algorithm to determine the optimal spatial arrangement of these habitat patches for visualization. This will provide a clearer depiction of how these habitats are interconnected and aid in understanding the overall network dynamics.

To accomplish this, you can utilize the NetworkX library, particularly its layout algorithms, to generate a visually informative representation of the habitat network. By mapping the nodes (habitats) and edges (connections) onto a two-dimensional plane, you'll be able to visualize the network topology and identify key areas of connectivity crucial for wildlife movement and ecological processes.
### Response:
import networkx as nx

# Generate a random graph
G = nx.fast_gnp_random_graph(8, 0.5)

# Create a layout for the graph
# Note: `_layout_mapping` is not part of the public API
# So this example uses `spring_layout` for demonstration
pos = nx.spring_layout(G)

# Output the position mapping
print(pos)
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